Computational Intelligence and Neuroscience

Volume 2016, Article ID 8469428, 11 pages

http://dx.doi.org/10.1155/2016/8469428

## A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection

^{1}CSE Department, National Institute of Technology Silchar, Assam, India^{2}CSE Department, Assam University Silchar, Assam, India^{3}CSE Department, National Institute of Technology Manipur, Manipur, India

Received 20 November 2015; Revised 1 February 2016; Accepted 9 February 2016

Academic Editor: Carlos Alberto Cruz-Villar

Copyright © 2016 Dalton Meitei Thounaojam 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.

#### Abstract

This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of parameter.

#### 1. Introduction

With the growth of the Internet, the generation of multimedia contents is also increasing. This leads to the problem of effective utilizing and managing the video data. Effective utilizing and managing of the multimedia contents need effective indexing and retrieval system. This is much more difficult in the case of video. For an effective video retrieval system, the content of the video should be understood so that proper indexing system can be created for better video retrieval. The content of the video can be taken by first performing the video segmentation, dividing the video into meaningful shots, and analyzing each feature of the segments (shots) which is the key feature of each segment. A scene is a combination of more than one shot with different camera angles or a combination of similar shots.

In video segmentation (shot boundary detection), the video is divided into meaningful scenes so that each scene can be analyzed for finding the key feature(s). Shot boundary detection mainly consists of finding the two types of transitions abrupt transition and gradual transition [1, 2]. Abrupt transition (also known as hard cut) is the sudden change of the consecutive frames in a video which marks the scene change due to sudden release of the camera rolling. Gradual transition (also known as soft cut) is of four types: fade-in, fade-out, dissolve, and wipe transitions. All these gradual transitions are a result of the editing effect in a video. Fade-in and fade-out are caused by the lightness value. In fade-in, a picture appears slowly from a darker (usually black) empty frame. In fade-out, a picture slowly diminishes to an empty frame (usually black frame). Dissolve and wipe transition is an effect due to overlapping of the current scene and the future scene. In dissolve, the overlapping is done in such a way that the current scene starts disappearing and the future scene starts appearing simultaneously. In wipe, the overlapping is done in such a way that the future scene grows over the current scene until the future scene appears completely.

#### 2. Related Works

Many researchers [1–3] have tried to detect the transitions (known as shot boundary detection or temporal video segmentation) in a video in compressed and uncompressed domain. MPEG (Motion Picture Expert Group) provides video formats which provide a large area of analyzing frame features in the compressed domain using motion vectors [4], Discrete Cosine Transform coefficients [5], and so forth. The frame feature extraction can be globally and locally. Global feature extraction considers the whole feature of the frame such as the pixel value [6]. Local feature extraction considers some regions of the frame and the features in that region are only taken or in other senses the necessary/important features of the whole frame are considered. MSER [7], SURF [8], and so forth are some of the popular local feature descriptor used for shot boundary detection. These features are extracted from each frame of the video and calculate the differences between consecutive frames to find out the transitions. The gradual transitions are rather difficult than the abrupt transition as it may have the same effect with large object motion and camera motion [1]. Thus, it is necessary to extract features which give less/no effect with large object motion, camera motion, or lighting effect.

Intensity histogram and Color Histogram Difference are of the effective, simple, and widely used methods for shot boundary detection in the uncompressed domain which is not sensitive to motion [6]. In [10, 11], SVD is applied to frame histogram matrix and a similarity measure is applied to find out the abrupt and gradual transitions. In [10], consecutive frames between two frames are skipped for analysis, which reduces the computational time drastically. In [9], HSV color histogram and an adaptive threshold are used for shot boundary detection and also the algorithm can detect flashes. In [8], entropy and SURF features are used to find the cut and gradual transitions where the intensity histogram is used to calculate the entropy of a frame.

Genetic Algorithm [12, 13] and Fuzzy Logic [6, 14, 15] have been used for shot boundary detection. In [16], color histogram is generated using Fuzzy Logic for abrupt and gradual transition detection. In [17], an Adaptive Fuzzy Clustering/Segmentation (AFCS) algorithm is proposed and the fuzzy clustering algorithm is used for image segmentation where it takes into account the inherent image properties like the nonstationarity and the high interpixel correlation. A Multiresolution Spatially Constrained Adaptive Fuzzy Membership Function is used for tuning the AFCS. In [18], Genetic Algorithm is used to generate the membership function of the fuzzy system for image segmentation.

In this paper, we introduced a method of shot boundary detection using Fuzzy Logic system optimized by GA. Fuzzy system is used to classify the video frames into different types of transitions (cut and gradual) using normalized Color Histogram Difference. GA is used as optimizer to find the optimal range of values of the fuzzy membership functions. The result shows that the combination of this feature is efficient and the accuracy increases with increase in iterations/generations of GA.

The paper is organized as follows. Section 3 explains the feature extraction of the system. A detail explanation of the GA optimized fuzzy system to find out that the range of values of the membership functions is given in Section 4. Experimental Results and Discussion and Conclusion are given in Sections 5 and 6, respectively.

#### 3. Feature Extraction

This section discussed the feature extraction used in our proposed system.

##### 3.1. Color Histogram Difference

Color histogram is a global feature extraction technique which is one of the simplest and widely used image feature extractions for shot boundary detection [19]. It is nonsensitive to motion [6, 14]. In [6], the normalized color histogram between two frames, say and frames, in a video is defined as follows:where is the number of pixels in a frame, is the number of red pixels of th frame in bin, and vice versa. , , and represent red, green, and blue components of a frame. It is observed that (1) yields a value with an interval . yields a value 0 when the and frames are same and the value goes on increasing as the similarity between and frames decreases.

#### 4. Fuzzy Logic System with GA Optimization for Finding the Value Range of the Membership Function

Genetic Algorithm (GA) is used as optimizer to find optimal values of the membership functions of the Fuzzy Logic system [20, 21]. The steps are shown as follows.

##### 4.1. Fuzzification

First we define the input and output variables of the fuzzy system.

The input variables are(a) is with linguistic values negligible (N), small (S), significant (Sig), large (L), and huge (H); Variable is the histogram difference value which is the difference between and frames and is computed using normalized histogram intersection;(b) is with linguistic values negligible (N), small (S), significant (Sig), large (L), and huge (H); Variable is the histogram difference value which is the difference between and frames;(c) is with linguistic values negligible (N), small (S), significant (Sig), large (L), and huge (H); Variable is the histogram difference value which is the difference between and frames.

The output variable is(a)transition with linguistic values no (NO), abrupt (AB), and gradual (GR). Variable transition is the type of transition that can occur from one frame to another. no represents the frame where there is no transition.

The rule base consists of 28 rules of the form as in [6]. In Table 1, rules for detecting no transition (frame without any transition) are given. For detecting gradual transition and abrupt transitions, the rules are provided in Tables 2 and 3, respectively.