Advances in Multimedia

Advances in Multimedia / 2018 / Article

Research Article | Open Access

Volume 2018 |Article ID 2056381 | 20 pages |

Commercial Video Evaluation via Low-Level Feature Extraction and Selection

Academic Editor: Alexander Loui
Received01 Aug 2018
Revised21 Sep 2018
Accepted24 Sep 2018
Published10 Oct 2018


To discover the influence of the commercial videos’ low-level features on the popularity of the videos, the feature selection method should be used to get the video features influencing the videos’ evaluation mostly after analyzing the source data and the audiences’ evaluations of the videos. After extracting the low-level features of the videos, this paper improved the Correlation-Based Feature Selection (CFS) method which is widely used and proposed an algorithm named CFS-Spearmen which combined the Spearmen correlation coefficient and the classical CFS to select features. The 4 datasets in UCI machine learning database were employed as the experiment data. The experiment results were compared with the results using traditional CFS, Minimum Redundancy and Maximum Relevance (mRMR). The SVM was used to test the method in this paper. Finally, the proposed method was used in commercial videos’ feature selection and the most influential feature set was obtained.

1. Introduction

As a major kind of commercial multimedia, commercial videos’ popularity is concerned by related companies and producers. There are many factors which influenced the audiences’ evaluation to the commercial videos, such as the actor/actress, director, and story. But it is too subjective if these factors are employed to evaluate the videos. It is because that these factors are not quantitative in the evaluation procedure. So, the importance of design a model to analysis the videos and calculate the evaluation of them should be realized by the researchers [1]. The article [2] proposed an objective video quality evaluation method based on motion and disparity information. The article [3] presented a video quality evaluation method based on Quaternion Singular Value Decomposition. But only a few video features were used in [2, 3]. The video features should include many features such as color features, motion features, and shot features which might be very likely to influence the audiences’ evaluation of the videos. However, the most influential feature set and the relationships between them and the video popularity are still not clear yet. For all the features extracted, we can select the most influential feature set through the feature selection methods according the commercial videos’ evaluation data. Of cause, the feature dimension reduction methods could also be used here.

Different feature set might be selected by different feature selection or dimension reduction methods. The feature dimension reduction algorithms, such as PCA [4, 5], FDA [6], and KPCA [7, 8], would reduce the feature number, while feature selection algorithms would select the optimal feature set from all the original features. Compared with the dimension reduction algorithms, the feature selection maintains the physical meanings of the original features and this is more convenient for data and relationship between features and videos’ popularity analysis [9].

The main idea of feature selection is selecting a few valuable features and removing the useless ones from all the features extracted. The methods, such as embedded methods, Relief [10], mRMR [11], and CFS [12, 13], are widely used now.

For the embedded methods, sparsity regularized feature selection methods were also widely used in some research area such as appearance modeling in visual tracking [1416]. In these methods, -norm [17] (being called Lasso, presented by Tibshirani in 1996) and -norm based regularization models have been researched for selecting features with joint sparsity across different tasks [18]. These methods select features by adding a penalty term with weight to the objective function of the machine learning model, which restricts the weight of each feature of the model. Feature selection is done in the process of model training, and those features whose coefficients are trained to zero are considered as redundant features. Recently, -norm and -norm model based regularization methods have attracted more and more attention because they can obtain sparser solutions than the methods based on 1-norm and 2,1-norm [19]. The algorithms typically take a trade-off between a data-fitting loss function term and a sparsity term; therefore there inevitably exists residual in the loss function. However, little is known about such a residual’s impact on the feature selection [20].

For other methods, only the correlation between feature and class was concerned in Relief algorithm but not the correlation between features. So, the selected feature set was not the optimal one [21]. For mRMR algorithm, both the correlation between features and feature-class were concerned to get the best feature set [22]. For the classifier, all the contributions of the features selected by mRMR were same, and the feature set was selected from the original features. The main idea of CFS is selecting the feature set with lower correlation between features and higher correlation between feature and class. After this procedure, the redundant features and the features which were not closely related to the class would be removed. The Pearson linear correlation coefficient was used in [21, 22] by Huanjing Wang and Ningqing Sun to calculate the correlation between features and feature-class. But only linear correlation coefficient could be measured in Pearson formula. For continuous data, discretization methods or kernel density estimation methods should be employed to solve the problem. This procedure would lead to probability estimation error.

So, the effectiveness of the correlation calculation is the key of successfulness of CFS. Currently, some correlation calculation methods were concerned by many researchers, and we should select the best one according to experiment results. Except Pearson Coefficient, some other correlation calculation methods are being used now. Spearman Rank Correlation Coefficient was used in the article of Marie Therese Puth [23] to descript the correlation of two vectors. The experiment result showed that the Spearman Rank Correlation Coefficient is better than Pearson Coefficient. In the research of Xiaoyuan Xu [24], the Spearman Rank Correlation Coefficient was employed to descript the correlation between features of wind speed. In the article of Jing Feng [25], a nonparametric method based on Spearman Rank Correlation Coefficient was proposed to measure the principle of storage degradation failure.

Spearman Rank Correlation Coefficient was not widely used in feature selection yet. In this paper, after extracting the commercial videos low-level features including color features, motion features, and shot features, a CFS-Spearman algorithm is presented and used to process the four datasets, including ‘Cancer’, ‘Glass’, ‘Bank’, and ‘Credit’, in the UCI machine learning database. The experiment results are compared with CFS and mRMR. The LibSVM classifier is utilized to test the effectiveness of CFS-Spearman. Then, the method in this paper is employed to select the low-level features of the commercial videos to predict the popularity of them. The results showed that the proposed method was better than CFS, mRMR, and p-norm based sparsity regularized feature selection.

2. Video Low-Level Features Extraction

2.1. Color Features

Color is an important feature of vision. The color feature set is combined with 10 features including means and variances of Brightness, Contrast, Saturation, Colorfulness, and Simplicity. Brightness is calculated by average the brightness of every pixel in every frame in HSV space. It is similar as Saturation calculation procedure. The Contrast could be expressed by the following formula: in which, r, g, and b represent red, green, and blue value of a pixel. Var is the variance calculation function [26].

Colorfulness is a parameter reflecting the combination of image’s color. It is defined asin which Mean is the function to get the average the input value [27].

To attract the audiences’ attention in the progress of movie making, the director and the cameraman always make the scenes simpler than the objects. The Simplicity is used to measure this character in some article. It is defined in [28] and the final Simplicity is the mean value of every frame.

2.2. Motion Features

Motion features reflect the changing rate of the scene or objects in the videos. It could be regarded as the moving speed between camera and the objects while shooting. In this article, the motion features are calculated as follows. Firstly, every frame is separated into blocks. The pixel barycenter of every block is get, and then, the frame n and the frame n+1 are compared to obtain the barycenter changing rate of the corresponding blocks in the two neighbor frames. The motion feature mean is the mean value of pixel barycenter coordinate changing and the motion feature variance is the variance of it [29].

2.3. Shot Features

Shot features are also important for video evaluation. To get every shot in a video, the key frames, which are located at the edge of the shots, of the video should be selected firstly [30]. We compare the color histogram of every two neighbor frames to calculate the similarity of them. After key frame selection, the four features, “Shot length mean”, “Shot number”, “Shot length variance”, and “Video length”, are calculated.

Then, the 16 features, including “Brightness mean”, “Contrast mean”, “Saturation mean”, “Colorfulness mean”, “Simplicity mean”, “Brightness variance”, “Contrast variance”, “Saturation variance”, “Colorfulness variance”, “Simplicity variance”, “Motion mean”, “Motion variance”, “Trailer length”, “Shot number”, “Shot length mean”, and “Shot length variance”, are get as shown in Figure 1.

3. Feature Selection Suing CFS

When the features are extracted, the relationship between features and video evaluation is still not so clear. It is because that some features influence video viewers’ evaluation but others do not. We should select the most influential feature set of the videos’ evaluation. Some feature selection methods might be introduced here.

3.1. mRMR

mRMR is a typical feature dimension reduction method which use mutual information to measure the correlation between two features and feature-class. The formulas are listed as follows: in which S is the feature set, is the feature number, c is the class index, is the mutual information between the feature and class c, and is the mutual information between the feature and the feature.

The mutual information between x and y here is defined as Then, we get the criterion of feature selection as follows:

3.2. CFS Algorithm

CFS is a simple feature selection method. It calculates the correlation value between every two features and feature-class to select the features related to classes most closely. As shown in (9) and (10), this method tries to maximize the Ms. In the formulas above, is a measure of feature set with k features. is the average correlation calculation method of x and y which are all features or feature and class. N is the number of samples. According to formula (10), in the feature set S, the value of will be bigger if the average correlation between features is minor and the average correlation between features and class is bigger. Then, the feature set is an optimized one. This algorithm is called Pearson Correlation Coefficient.

3.3. Spearman Rank Correlation Coefficient

Pearson Correlation Coefficient was employed in traditional CFS. But there are some other correlation calculation methods which could be tested. Spearman Rank Correlation Coefficient is one of them. It could be defined as follows:In the formula above, we firstly define the random variable X and Y with N samples as . Then, let and be the ranks of and in the corresponding sample. and are the average ranks of the sample. The Spearman Rank Correlation Coefficient described the monotonic dependence of variables X and Y. The monotonic direction could be recognized by the sign of . When Y creases with X creasing, the sign of is positive, and it is negative conversely. Y will not change with X while the sign of is 0.

The linear correlation coefficient is a widely used correlation measurement and it is easy to be calculated. When the random variables are elliptical distribution, the linear correlation coefficient could express the correlation between the variables. But the short points of it is that it is nonexistent when the variables’ first- and second-order moments could not be get, its value should change when the variables distribution function changed, and after nonlinear strictly increasing, the linear relationships between variables would change [24]. It is most important that the relationship could not be expressed accurately while the variables do not distribute as elliptical distribution.

The Spearman Rank Correlation Coefficient is a nonparametric statistic method. Let the rank correlation coefficient of the two variables X and Y be , thenin which and are the cumulative probability of and , respectively.

The two random variables’ rank correlation coefficient is the linear correlation coefficient of the cumulative probability distribution function expressed as . If the inverse function of it exists, the variable distributed as uniform distribution in because of the following formula:So the rank correlation coefficient is just an expression of relationship after transformation from the original variables to the variables in uniform distribution. Compared with linear correlation coefficient, the rank correlation coefficient’s advantages are as follows: always exist; does not change with edge distribution; does not change after strict nonlinear transform. So, it is chosen in this paper to measure the relationships between feature and feature or feature and class.

4. Experiment Analyzing for Feature Selection

To prove the effectiveness of CFS-Spearman feature selection method in this paper, the 4 datasets, including “Breast-cancer”, “Glass”, “Bank”, and “Credit” in the UCI machine learning database, are used as the experiment data. The detailed information of the 4 datasets is listed in the Table 1. In the experiment procedure, CFS-Spearman feature selection method was employed to select the most important features and the SVM classification was used to test the selected feature sets. In the experiment, 1/10 of all the samples were selected randomly as the testing data and the others were used as the training data. This classification procedure was repeated for 10 times for every dataset. The mean values of the correctly classification rates were showed in the tables and figures to prove the effectiveness of the method in this paper.

Data setSample NumberFeature numberClass number


The experiment results were got by Matlab and LibSVM tool box. The results of CFS-Spearman feature selection were also compared with the original CFS, mRMR, and p-norm based sparsity regularized feature selection method as shown in the tables. In Tables 3 and 8, the weighted fonts are the correctly classification rate of CFS-Spearman higher than or equal to CFS, mRMR, and p-norm. For p-norm based sparsity regularized feature selection, we obtained the different settings of in every experiment. This parameter was selected by the classification results using SVM.

Table 2 showed the 8 features and classes of dataset “Breast-cancer”. As shown in Table 3, the CFS-Spearman feature selection method, original CFS, mRMR, and p-norm (p=0.9) were used to select the features in this dataset. From the results, it is obvious that the selected features were different in most feature sets. But if using features selected by CFS-Spearman, the correct classification rates were higher than or equal to other methods in all cases.


AgeMenopauseTumour sizeTumour nodeCanceration degreeTumour positionTumour quadrantRadiotherapy or notBenign or malignant

Feature numberCFS-SpearmanCFSmRMRp-norm (p=0.9)
FeaturesCorrect Classification rate ()FeaturesCorrect Classification Rate ()FeaturesCorrect Classification rate ()FeaturesCorrect Classification rate ()


In the second experiment, the dataset is “Glass”. We can estimate whether a piece of glass is “float glass” or not according to the chemical elements in it. These elements were listed in Table 4. Same as the second experiment, the third one is about one person might be a member of bank or not. The 9 features, like “Age”, “Living area”, “Income”, and so on, were listed in Table 5. The last experiment was about “Credit degree”. There were 14 features in this dataset as shown in Table 6.


IridiumSodiumMagnesiumAluminiumSiliconPotassiumCalciumBariumIronFloat glass or not


AgeLiving areaIncomeMarital statusChildren umberHave a Car or notDepositAccount exchangeMortgageBank member or not


Keeping TimeCredit recordPurposeLimitInstalment promisePersonal informationResidenceCredit degree

Property rightsAgeHousingJobFamily numberCell phoneForeign worker or notCredit degree

The correct classification rates were shown in Figure 2. In the figure, (a) showed the classification results of dataset “Breast-cancer”. The red curve is the correct classification rate of CFS in the cases of 1-feature, 2-features, to 7-features. The green curve was for mRMR and the blue one was for CFS-Spearman. The pink curve was for p-norm (p=0.9). The results in this subfigure are same as the data in Table 3.

Figure 2(b) showed the classification results of dataset “Glass” using different feature selection methods. Correct classification rates of CFS-Spearman algorithm in cases of 1-feature, 2-features, to 7-features were better than original CFS, mRMR, and p-norm (p=0.7). But if we select 8 features, the correct classification rate of CFS-Spearman algorithm was lower. When 4 features were selected, the correct classification rate was highest and the feature set included features 1, 3, 4, and 5 corresponding to Table 4.

As shown in Figure 2(c), for dataset “Bank”, the correct classification rates of CFS-Spearman algorithm in cases of 1-feature, 2-features, to 8-features were better than or equal to original CFS, mRMR, and p-norm (p=0.7). The correct classification rates were all 81.25%. Corresponding to Table 5, when we selected 2 features, the set is constructed by feature 1 and feature 6. When 4 features were selected, the set is combined with feature 1, feature 6, feature 7, and feature 8.

Figure 2(d) showed the classification results of dataset “Credit” using different feature selection methods. Correct classification rates of CFS-Spearman algorithm in cases of 1-feature, 2-features, to 13-features were better than or equal to original CFS, mRMR, and p-norm (p=0.9). When more than 9 features were selected, the correct classification rate reached the highest value 84.2%, and the 9-features set included features 3, 5, 6, 7, 9, 11, 12, 13, and 14 corresponding to Table 4.

From Figure 2, in most cases, the correct classification rates increased with the increasing of feature number until one feature number and then decreased. So, if we want to make a classification with some features like those shown in Table 8, not all features are necessary. Some more important features should be selected by the appropriate feature selection method.

5. Videos Popularity Prediction

In this paper, the CFS-Spearman feature selection method was used in video low-level feature selecting for videos’ popularity prediction. The low-level feature set included 16 features as expressed in the second part of this paper. There are 300 videos, which were downloaded from “Youtube”, used in experiments. The 16 features were extracted for each video and the serial numbers of them were showed in Table 7.


Mean of motionVariance of motionMean of brightnessMean of contrastMean of saturationMean of colorfulnessMean of simplicityVariance of brightnessGood) Bad(0)


Variance of contrastVariance of saturationVariance of colorfulnessVariance of simplicityMean of shot lengthVariance of shot lengthShot numberVideo lengthGood) Bad(0)

Feature numberCFS-SpearmanCFSmRMRp-norm (p=0.9)
Feature setCorrect classificat-ion rate (%)Feature setCorrect classificat-ion rate (%)Feature setCorrect classificat-ion rate (%)Feature setCorrect classificat-ion rate (%)


To evaluate the popularity of them, the audiences’ “Like/Dislike” votes numbers were employed to calculate the popularity degree as shown inin which LN is the vote number of “like” and DN is the vote number of “dislike” for a video. We set 4.7 as the threshold of classification for “good” videos and “bad” videos. It means that if the score of a video is higher than or equal to 4.7, it is a good video. Otherwise, it is regarded as bad one. All the extracted features were listed in Table 7. We used the four methods to select the features, and the selecting results were shown in Table 8. If the selected features were employed to classify the videos in to two classes, good and bad, the correct classification rates were listed in Table 8.

The SVM classification results showed that most correct classification rates of CFS-Spearman method were higher than that of the other two ones. And when the feature number is 3, the correct classification rates are the highest one which is 78%. And in this case, the features “Mean of contrast”, “Variance of Simplicity”, and “Variance of shot length” were selected as the most influential features. So, they could be used as the feature set to predict the popularity of commercial videos.

The correct classification rates curves were drawn in Figure 3.

The results in Table 8 were obtained by SVM using the selected feature set. Recently, some classification methods associated with deep learning received wide attention. CNN (convolution neural network) is a popular one within them. To compare the classification effectiveness, CNN was designed to test the prediction of the videos’ popularity in this part using the selected 3 features. The design of CNN used in this paper was shown in Figure 4. Firstly, the selected feature set, including features 1, 3, 4, 5, 6, and 7, were employed as the original input data of CNN. It is because that other features, such as “Variance of motion” and “Variance of shot length”, were just one scalar but not a vector or matrix.

In contrast with using SVM, the 6 features (features 1, 3, 4, 5, 6, and 7) were not the mean values of every frame in the whole video, but just uniformly selected 500 frames from a video and calculated the 6 features in every selected frame. Then, we get the input data whose size is 500×6 of CNN. The parameters in this framework were set as follows. The convolution and pooling procedure was repeated for 3 times. For the 6 features, there was no any relationship between each other. So, the convolution kernels’ size was set as [m, . The parameters were designed as follows.

For the first time, m1=51, k1=500, and there were 4 convolution layers in the procedure. Then, the size of feature map C1was 4@(450×6). After the first pooling processing with p=2, the size of feature map S1 was 4@(225×6). For the second time, m2=76, k2=225, and there were 10 convolution layers. The size of feature map C2 was 10@(150×6). The size of feature map S1 was 10@(75×6). For the third time, m3=75, k3=75, and the size of feature map S3 was 10@(1×6). We convert S3 to a column shoes size which was (60×1). Then, the video’s 3 features were transformed to a new feature set with 60 numbers. After Softmax classification, the results were obtained. The experiments were repeated for 10 times, as shown in Table 9, just like the experiments using SVM. The testing result showed that the mean correct classification rate was 61.6667, which was much lower than SVM using the 3 selected features. It is because that the 16 features were most scalars which were not suitable for CNN classification.

Correct classification rate (%)Mean


If we use the whole videos or some frames in them as the input data for CNN, according to the characteristic of CNN, it only pays attention to the differences between the input data in the convolution procedures or tries to get the edges in the images or videos. So, the features such as the 3 selected one, which were proved to be the influential features to the popularity of the videos, would not be calculated in the CNN framework.

6. Conclusions

This paper researched the low-level feature selection methods in commercial videos’ popularity prediction. To select the more influential features in the feature set, the paper proposed a CFS-based method in which the Spearman Correlation Coefficient was employed to take place of original Pearson Correlation Coefficient. To test the method, the data in UCI machine learning database were used as the experiments data source. The widely used algorithm, mRMR, original CFS, -norm based sparsity regularized feature selection, and the method called CFS-Spearman algorithm presented in this paper were compared using the data. Four results showed that the method in this paper was better than the other ones.

Then, to select the influential low-level features for commercial videos, 300 videos were downloaded from Youtube. For each video, 16 features were extracted, and the videos were separated into two classes, “good” and “bad” according to their scores which were calculated through the “like/dislike” number voted by audiences. When the selected feature number is 3 by CFS-Spearman, the correct classification rates is the highest one which is 78%. The features called “Mean of contrast”, “Variance of Simplicity”, and “Variance of shot length” were selected as the most influential ones.

Finally, the SVM classification was compared with the popular classification method CNN. The results showed that because the 16 features were most scalars, the SVM was more suitable for this target.


See Table 10.


1Chairman of the Board Trailer
2“Daniel der Zauberer” (official trailer) HQ
3Dinoshark (2011) - Official Trailer
4From Justin To Kelly Trailer
5Gigli (2003) Trailer
6Growth - Official Trailer [HD]
7House of The Dead - Official HD Movie Trailer
8House Of The Dead Trailer (2003)
9A Fox's Tale (Kis Vuk) English Trailer
10Jack and Jill Trailer 2011
11Kaboom Movie Trailer 2011 Official Trailer
12Manborg (2012) - Official Trailer - Horror Movies HD
13Mars Needs Moms Movie Trailer (HD)
14Mega Python VS Gatoroid (2011) - Official Trailer
15Melissa Molinaro - The Hillz (2004) Trailer
16Outpost 31 - 2013 OFFICIAL Trailer
17Back to the Future 4 Official Trailer HD 720p
18piranhaconda trailer
19Quantum Apocalypse 2010 Trailer
20Rise of the Animals (2012) - Official Trailer - Horror Movies HD
21RoboCop 2013 Trailer (official)
22Sand Sharks Trailer
23Sharktopus (2010) - Official Trailer [HD]
24Son of The Mask 2005 High Quality
25Son of the Mask Movie Trailer
26Superbabies: Baby Geniuses 2 (2004) Trailer [Official]
28Bad ass (2012) Official Trailer HD
29The Double (2011) Movie Trailer HD
30The Fifth Element (1997) Trailer
31The Final Sacrifice Trailer
32The Hottie and The Nottie Movie Trailer
33The Human Centipede - Official Movie Trailer 2010 [HD]
34The Legend of the Psychotic Forest Ranger (2012) - Official Trailer
35Bad Kids Go to Hell Official Trailer 2 (2012)
36Titanic: The Legend Goes On Trailer
37Tony Blair Witch Project - Trailer
38Trailer - NOOBZ - Official Movie Trailer HD (2012)
39Transformers 4: The Return of Megatron Trailer HD
40Yes Sir! Trailer (Official Version)
41YOUNG ADULT Trailer 2011 Official [HD]
42Zombie Nation Trailer
43Battlefield America - Official Exclusive Trailer [HD]
44Ben _ Arthur - Trailer
45Book of Shadows Blair Witch 2 [a steFANedit] TRAILER
46All Superheroes Must Die Official Trailer #1 (2013) - Jason Trost Mo
47New Year's Eve Movie CLIP #2 - This is Not a Training Bra! (2011) H
48World Of Heroes - Exclusive Superbowl XLIV TV Spot - TRUE 720 HD
49Re-Kill Official Trailer (2011 Movie)
50“Snow Doesn't Melt Forever...” (official trailer)
51District 13: Ultimatum' Trailer HD
52Cosmopolis (2012) - Official Trailer [HD]
53Four (2012) - Official Trailer [HD]
54Dark Tide (2012) - Official Trailer [HD]
55Griff the Invisible (2010) Movie Trailer - HD
56Zone Of The Dead - Official Trailer
57The Wicked Official Teaser Trailer (2012) Horror Movie HD
58The Troll Hunter - Official Trailer
59American Hustle Trailer
60Hammer of the Gods Official Trailer #1 (2013) - Viking Movie HD
61Tiptoes (2003)
62Superbabies: Baby Geniuses 2 (2004)
63Sharktopus (2010)
64Disaster Movie (2008)
65Vampire Dog (2012)
66Super Mario Bros (1993)
67Empire Of The Apes (2013)
68C Me Dance (2009)
69Santa With Muscles (1996)
70Quigley (2003)
71Dinocroc Vs Supergator (2010)
72The Hottie & The Nottie (2008)
73Hobgoblins (1988)
74Samurai Cop (1989)
75Butterfinger The 13th (2011)
76Transmorphers (2007)
77LOL (2012)
78Anaconda 4: Trail Of Blood (2009)
79Mega Python Vs Gatoroid (2011)
80A Talking Cat (2013)
81Battlefield America (2012)
82Mega Shark Vs Giant Octopus (2009)
83Abraham Lincoln: Vampire Hunter (2012)
84Old Dogs (2009)
85Rottweiler (2004)
86I Hate Valentine’s Day (2009)
87The Room (2003)
88Age Of The Hobbits (2012)
89Leprechaun (1993)
901 ROBOCOP - Official Trailer (2014) [HQ]
91White House Down - Official Trailer (2013) [HD] Channing Tatum, Jam
92Scary Movie 5 Official TRAILER #1 (2013) - Charlie Sheen, Ashley Tis
93Spiders 3D Official Trailer #1 (2013) - Science Fiction Movie HD
94Evil Dead Trailer 2013 Movie - Official [HD]
95SHARKNADO - Official Asylum Trailer - TOO VIOLENT FOR TV
96Bad Milo Official Trailer #1 (2013) - Ken Marino Comedy HD
97Hell Baby Official Trailer #1 (2013) - Horror Comedy Movie HD
99Killing Season Official Trailer #1 (2013) - Robert De Niro, John Travolt
100Iron Sky Official Theatrical Trailer [HD]
101Adore Official Trailer #1 (2013) - Robin Wright, Naomi Watts Movie H
102Lincoln Trailer
103Behind the Candelabra Trailer (Matt Damon - Michael Douglas )
104Assault on Wall Street Official Trailer #1 (2013) - Dominic Purcell, Eri
105Chennai Express | Official Trailer 2013 | Shah Rukh Khan | Deepika P
106Rapture-Palooza Official Trailer #2 (2013) - Anna Kendrick Movie HD
107∗The Smurfs 2∗ Official Trailer #1 Starring Neil Patrick Harris (2013) [HD]
108Temptation Official Trailer #1 (2013) - Tyler Perry Movie HD
109The Tree of Life Movie Trailer Official (HD)
1103 Geezers! Trailer
111The Stranger Within Exclusive Official Trailer #1 (2013) HD william bal
112Jug Face Official Trailer 1 (2013) - Horror Movie HD
113ATLANTIC RIM trailer
114Twilight Breaking Dawn Part 2 Official Trailer
115Zero Dark Thirty - Official Trailer #2 (HD)
116NTR Jr Ramayya Vasthavayya | Theatrical Trailer | Jr NTR, Samatha
117Shuddh Desi Romance - Official Theatrical Trailer - Sushant | Parineet
118The Demented Official Trailer 1 (2013) - Horror Movie HD
119Albatross Official Trailer #1 (2012) HD
120House of Pleasures Official Trailer #1 - L'Apollonide Movie (2011) HD
121Official Call of Duty®: Ghosts Single Player Campaign Trailer
122Magic Magic Official Trailer #1 (2013) - Michael Cera Movie HD
123Wadjda Offical Trailer (2013) - Haifaa Al Mansour
124The Colony Official International Trailer #1 (2013) - Laurence Fishburn
125Eden Movie Trailer
126Thanks For Sharing Official Trailer #1 (2013)
127Trailer: RoboCop TRAILER 1 (2014)
128Need for Speed Official Trailer
129NEED FOR SPEED Movie Trailer
130Trailer: Her TRAILER 1 (2013) - Joaquin Phoenix, Scarlett Johan
131PARADISE Movie Trailer
132Jobs Official Trailer #1 (2013) - Ashton Kutcher Movie HD
133The To Do List Trailer 2013 Movie - Official [HD]
134Ender's Game Official Trailer #2 (2013) - Asa Butterfield, Harrison
135Scary Movie 5 Official TRAILER #1 (2013) - Charlie Sheen, Ashle
136BOUNTY KILLER Movie Trailer
137ANOTHER EARTH trailer 2011 official movie
138Behind the Candelabra Trailer (Matt Damon - Michael Douglas )
139Empire State Official Trailer #1 (2013) - Dwayne Johnson, Liam Hems
140Haunter Official Trailer #1 (2013) - Abigail Breslin Movie HD
141Baggage Claim Trailer 2013 Paula Patton Movie - Official [HD]
142Special Forces Movie Trailer (2012)
143WINNIE MANDELA Movie Trailer
1447500 - Official Movie Trailer
145Biryani official teaser | Karthi, Hansika, Venkat Prabhu, Yuvan, Pre
146Date Movie - Trailer (2006)
147Emperor Movie Trailer (2013)
148Squirrels Teaser Trailer (2014) - Squirrel Horror Movie HD
149BA PASS Official Movie Trailer
150Bangaru Kodi Petta - Telugu Movie Trailer - Swathi Reddy & Navdeep
151Ainthu Ainthu Ainthu Movie Trailer - Nikhils Channel
152Michael Bay's THE LAST SHIP Series Trailer
153Monsters Movie Trailer Official (HD)
154The Tree of Life Movie Trailer Official (HD)
155Fired Trailer | Bollywood Adult Horror Movie | Rahul Bose, Militza Rad
156Battle of the Year Trailer 2013 Movie - Official [HD]
157jOBS Movie CLIP (2013) - Ashton Kutcher Movie HD
158Vamps Movie Trailer
159Naked As We Came (2012) Movie Trailer (Gay Themed) Eshsiz.com
160Evil Dead Trailer 2013 Movie - Official [HD]
161NABAR - New Punjabi Movie | Official Theatrical Trailer | Latest Punj
162Raja Huli Promo Kannada | Yash, Meghana Raj | Latest Kannada Mo
163Lucky (2011) - Movie Trailer - HD
164Samrajyam 2 Son Of Alexander Malayalam Movie Trailer | Unni Muk
165Metro Official Trailer #2 (2013) - Russian Disaster Movie HD
166Once Upon A Time In Shanghai Official Movie Trailer 2013 in HD
167Directors special Kannada movie Latest Trailer - Guru Prasad,Ara
168Spring Breakers Trailer 2013 Selena Gomez Movie - Official [HD]
169The Assassins Movie Trailer (2013)
170SHELL (film 2013) official UK trailer
171New Moon Movie Trailer - Official (HD)
172Prom Movie Trailer Official (HD)
173Youth of Christ- The Movie Trailer
174Universal Soldier 4 Red Band Trailer
175Sahara - Movie Trailer
176Hardwired movie Trailer 2009
177Official Watchmen Trailer
178Casino Royale teaser
179The official Cloverfield trailer
180Terminator 4 Salvation teaser trailer 2009 official
181Battle Los Angeles - Official Movie Trailer #1 (2011) US | HD
182Mission: Impossible Trailer HQ (1996)
183Marvel's The Avengers Super Bowl XLVI Commercial
184Iron Man 3 Trailer UK - Official Marvel | HD
185Sleepless In Seattle: Recut as a horror movie
186X-Men: First Class Movie Trailer Official (HD)
187Harry Potter and The Deathly Hallows Part 2 Trailer Official (HD)
188Bubble Trailer
189House of The Dead - Official HD Movie Trailer
190Paranormal Activity 2' Trailer
191Pirates of the Caribbean 3 - At World's End Trailer
192District 9 - Official Trailer 2 [HD]
193The Departed - Trailer - (2006) - HQ
194Iron Man 2 Trailer
195THE CRAZIES - Trailer
196The Dark Knight HD 1080p Trailer
197‘Inception’ Trailer 2 HD
198The Dark Knight Rises - Official Trailer #3 [HD]
199The Hobbit: An Unexpected Journey
200World War Z Official Trailer #1 (2013) - Brad Pitt Movie HD
201Independence Day - Official® Trailer [HD]
202Prometheus - Official Full HD Trailer
203Transformers 3 Dark of the Moon Teaser Trailer - Official (HD)
204Rise of the Planet of the Apes - HD Trailer 2 - (2011)
205Harry Potter and the Sorcerer's Stone Trailer
206RESIDENT EVIL 5 Retribution Trailer 2 - 2012 Movie - Official [HD]
207TOTAL RECALL Trailer 2012 Movie - Official [HD]
208Trailer: GREAT GATSBY Trailer (2012) Movie HD
209THE LUCKY ONE Trailer 2012 Movie - Official [HD]
210TED Movie Trailer 2012 - Official [HD]
211FLIGHT Trailer 2012 Denzel Washington Movie - Official [HD]
212Killing Them Softly Trailer 2012 Brad Pitt Movie - Official [HD]
213Jarhead Trailer HD
214Stand Up Guys Official Trailer #1 (2012)
215Trailer: Pitch Perfect Trailer (2012)
216Seven Psychopaths Official Trailer #1 (2012)
217RUBY SPARKS Trailer 2012 Movie - Official [HD]
218Wreck-It Ralph Official Trailer #1 (2012)
219Trailer: Hit And Run Official Trailer #1 (2012)
220The Amazing Spider-Man New Trailer 2 Official 2012 HD]
221The Lone Ranger Official Trailer #2 (2012) - Johnny Depp Movie HD
222THE CAMPAIGN Trailer 2012 - Will Ferrell movie - Official [HD]
223Pitch Perfect Trailer Clip - 2012 Movie - Official [HD]
224Hitchcock Trailer 2012
225Forrest Gump Trailer (Movie release: July 6, 1994)
226Trailer: The Butterfly Room TRAILER 1 (2012)
227Best New Movie Trailers - February 2012 HD
228The Imposter - Official Trailer HD (2012)
229Iron Man 2 Trailer 2 (OFFICIAL)
230500 Days of Summer
231Pirates of the Caribbean: On Strange Tides - Trailer 1
232SOURCE CODE - Trailer
233Official SALT Trailer - In Theaters 7/23/2010
235John Carter Trailer
236JUST WRIGHT - Official trailer
237Bad Teacher- Trailer
238Supernatural Comedy Trailer HD
239Predators Trailer
240GASLAND Trailer 2010
241I Spit On Your Grave (2010) - Official Trailer [HD]
242The Social Network Official Trailer
243New Beastly Movie Trailer - Official
244“Shutter Island” - Official Trailer [HD]
245Carriers Trailer(HD)
246The Crazies - Official Trailer [HD]
247“Brothers” - Official Trailer [HQ HD]
248SURROGATES - Bruce Willis
249Julie & Julia - Official Trailer
250LITERAL Tron Legacy Trailer Parody
251The Hurt Locker - Official Trailer [HD]
2528 Mile Official Trailer #1 - (2002) HD
253Road To Perdition Trailer (2002) [HD]
254A Walk To Remember Official Trailer (HD)
255Spider Man - Trailer
256Immortals - Official Trailer [HD]
257The Tree of Life Movie Trailer Official (HD)
258What's Your Number? Official Movie Trailer 2011 HD
259The Darkest Hour Trailer 2011 Movie Official HD
260The Bucket List Official Trailer #1 - (2007) HD
261Halloween 2007 Trailer (HD)
262ANOTHER EARTH trailer 2011
263John Carter Fan Trailer 2 “Heritage”
264REC 2 - Official Trailer [HD]
265GONE Trailer 2012 - Amanda Seyfried Movie - Official [HD]
266The Human Experience - Official Trailer [HD]
267Paranormal Activity 4 Trailer # Extended
268Casino Royale - Official® Trailer [HD]
269Serenity (2005) Trailer 1080p HD
270Harry Potter and the Goblet of Fire - Official Trailer 720p HD
271Motorstorm E3 2005 trailer
272The Bourne Supremacy - Official HD Trailer
273Being Flynn Official Trailer #1
274Puss in Boots (2011)
275I'll Be Seeing You (2004) HD
276Iron Man 3 Official Trailer (2013) Marvel Movie HD
277A Haunted House Official Trailer #1 (2013)
278Now You See Me Official Trailer #1 (2013)
279Pacific Rim Official Trailer #1 (2013)
280The Place Beyond the Pines Official Trailer #1 (2013)
281The Tower Official Trailer #1 (2013)
282Star Trek Into Darkness (NEW) Official Trailer (2013)
283Die Hard 5 Official Trailer (2013)
284Identity Thief Trailer 2012 Official [HD]
285Red 2 Official Trailer #1 (2013)
286Safe Haven Trailer 2013 Movie Nicholas Sparks - Official [HD]
287The Heat Official Trailer #1 (2013)
288Anchorman 2: The Legend Continues Official Teaser (2013)
289The Host Trailer #2 - 2013 Movie - Official [HD]
290Jack the Giant Killer Trailer 2012 Official 2013 Movie [HD]
291p] Official Rurouni Kenshin Action Movie Promo Trailer 2012
292TRON: LEGACY Official Trailer
293Trailer: Man of Steel Teaser
294Trailer: Oz the Great and Powerful TRAILER (2013)
295End Of Watch Official Trailer #1 (2012) Jake Gyllenhaal Movie HD
296Dark Shadows Trailer (Tim Burton)
297The Man With The Iron Fists Trailer 2012 Movie - Official [HD]
298The Dark Knight Rises trailer 2012
299SKYFALL - Official Teaser Trailer
300MEN IN BLACK 3 - Official Trailer

Data Availability

The data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare that they have no conflicts of interest.


The research work of this paper was supported by the Science and Technology Development Program Fund of Science and Technology Department Jilin province, China (no. 20150414051GH).


  1. L. Xiang-yu, Research on No-reference Video Quality Assessment Method, Zhejiang University, 2012.
  2. C.-Y. Ma, S.-M. Li, R.-Z. Ma, D. Zhu, and X.-X. Guo, “Objective assessment of stereo video quality based on motion and disparity information,” Journal of Optoelectronics Laser, vol. 24, no. 10, pp. 2002–2009, 2013. View at: Google Scholar
  3. F.-Q. Zhang, J.-L. Li, G. Li, J.-J. Man, and G. Chen, “Video quality assessment based on quaternion singular value decomposition,” Acta Electronica Sinica, vol. 39, no. 1, pp. 219–223, 2011. View at: Google Scholar
  4. S. Huang, D. Yang, G. Yongxin, and X. Zhang, “Combined supervised information with PCA via discriminative component selection,” Information Processing Letters, vol. 115, no. 11, pp. 812–816, 2015. View at: Publisher Site | Google Scholar | MathSciNet
  5. A. Vinay, C. Kumar A, G. Shenoy R et al., “ORB-PCA based feature extraction technique for face recognition,” Procedia Computer Science, vol. 58, pp. 614–621, 2015. View at: Google Scholar
  6. E. V. Patridge, P. C. Gareiss, M. S. Kinch, and D. W. Hoyer, “An analysis of original research contributions toward FDA-approved drugs,” Drug Discovery Therapy, vol. 20, no. 10, pp. 1182–1187, 2015. View at: Publisher Site | Google Scholar
  7. S. A. Vinay, V. S. Shekhar, K. N. B. Murthy et al., “Face recognition using gabor wavelet features with PCA and KPCA-A comparative study,” Procedia Computer Science, vol. 57, pp. 650–659, 2015. View at: Google Scholar
  8. Z. Xiao-guang, S. Zheng, X. Gui-yun et al., “A feature selection algorithm combining within-class variance with correlation measure,” Journal of Harbin Institute of Technology, vol. 3, no. 03, pp. 132–136, 2011. View at: Google Scholar
  9. Z. Dechuan and Z. Zhihua, “A correlation projection score-based feature selection algorithm,” Journal of Frontiers of Computer Science and Technology, vol. 1, no. 2, pp. 138–145, 2007. View at: Google Scholar
  10. S. Dalai, B. Chatterjee, D. Dey, S. Chakravorti, and K. Bhattacharya, “Rough-set-based feature selection and classification for power quality sensing device employing correlation techniques,” IEEE Sensors Journal, vol. 13, no. 2, pp. 563–573, 2013. View at: Publisher Site | Google Scholar
  11. H.-H. Hsu and C.-W. Hsieh, “Feature selection via correlation coefficient clustering,” Journal of Software , vol. 5, no. 12, pp. 1371–1377, 2010. View at: Google Scholar
  12. P. Moradi and M. Rostami, “A graph theoretic approach for unsupervised feature selection,” Engineering Applications of Artificial Intelligence, vol. 44, pp. 33–45, 2015. View at: Publisher Site | Google Scholar
  13. Z. Li, J. Liu, Y. Yang, X. Zhou, and H. Lu, “Clustering-guided sparse structural learning for unsupervised feature selection,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 9, pp. 2138–2150, 2014. View at: Publisher Site | Google Scholar
  14. X. Lan, A. J. Ma, and P. C. Yuen, “Multi-cue visual tracking using robust feature-level fusion based on joint sparse representation,” in Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '14), pp. 1194–1201, Columbus, OH, USA, June 2014. View at: Publisher Site | Google Scholar
  15. X. Lan, A. J. Ma, P. C. Yuen, and R. Chellappa, “Joint sparse representation and robust feature-level fusion for multi-cue visual tracking,” IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 5826–5841, 2015. View at: Publisher Site | Google Scholar | MathSciNet
  16. X. Lan, S. Zhang, P. C. Yuen, and R. Chellappa, “Learning common and feature-specific patterns: a novel multiple-sparse-representation-based tracker,” IEEE Transactions on Image Processing, vol. 27, no. 4, pp. 2022–2037, 2018. View at: Publisher Site | Google Scholar | MathSciNet
  17. R. Tibshirani, “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 58, no. 1, pp. 267–288, 1996. View at: Google Scholar | MathSciNet
  18. S. Xiang, F. Nie, G. Meng, C. Pan, and C. Zhang, “Discriminative least squares regression for multiclass classification and feature selection,” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 11, pp. 1738–1754, 2012. View at: Publisher Site | Google Scholar
  19. D. Kong and C. Ding, “Non-convex feature learning via lp,∞ operator,” in Proceedings of the National Conference on Artificial Intelligence, pp. 1918–1924, July 2014. View at: Google Scholar
  20. H. Peng and Y. Fan, “Direct sparsity optimization based feature selection for multi-class classification,” in Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI '16), pp. 1918–1924, July 2016. View at: Google Scholar
  21. H. Wang, T. M. Khoshgoftaar, and A. Napolitano, “Stability of filter- and wrapper-based software metric selection techniques,” in Proceedings of the 15th IEEE International Conference on Information Reuse and Integration (IEEE IRI '14), pp. 309–314, Redwod City, Calif, USA, August 2014. View at: Google Scholar
  22. S. Ningqing, “A network intrusion detection system based on neural networks and the cfs-based feature selection,” Computer Engineering & Science, vol. 32, no. 6, pp. 37–39, 2010. View at: Google Scholar
  23. M.-T. Puth, M. Neuhäuser, and G. D. Ruxton, “Effective use of Spearman's and Kendall's correlation coefficients for association between two measured traits,” Animal Behaviour, vol. 105, pp. 77–84, 2015. View at: Publisher Site | Google Scholar
  24. X. Xu, Z. Yan, D. Feng, Y. Wang, and L. Cao, “Probabilistic load flow calculation based on rank correlation coefficient of input random variables,” Automation of Electric Power Systems, vol. 38, no. 12, pp. 54–61, 2014. View at: Google Scholar
  25. J. Feng, “Consistent test of accelerated storage degradation failure mechanism based on rank correlation coefficient,” Journal of Aerospace Power, vol. 26, no. 11, pp. 2439–2444, 2011. View at: Google Scholar
  26. M. J. Swain and D. H. Ballard, “Color indexing,” International Journal of Computer Vision, vol. 7, no. 1, pp. 11–32, 1991. View at: Publisher Site | Google Scholar
  27. S. Hasler and S. Susstrunk, “Measuring colorfulness in real images,” vol. 5007, pp. 87-95, 2003. View at: Google Scholar
  28. J. S. Pedro and S. Siersdorfer, “Ranking and classifying attractiveness of photos in folksonomies,” in Proceedings of the 18th International World Wide Web Conference (WWW '09), pp. 771–780, Madrid, Spain, April 2009. View at: Google Scholar
  29. L. Yiwen and T. Xiaoou, “Photo and video quality evaluation: focusing on the subject,” in Proceedings of the European Conference on Computer Vision, part 3, pp. 386–399, Marseille, France, October 2008. View at: Google Scholar
  30. S. K. Kuanar, R. Panda, and A. S. Chowdhury, “Video key frame extraction through dynamic Delaunay clustering with a structural constraint,” Journal of Visual Communication and Image Representation, vol. 24, no. 7, pp. 1212–1227, 2013. View at: Publisher Site | Google Scholar

Copyright © 2018 Xiangmin Lun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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