Assessing the Effect of Data Augmentation on Occluded Frontal Faces Using DWT-PCA/SVD Recognition AlgorithmRead the full article
Advances in Multimedia publishes research on the technologies associated with multimedia systems, including computer-media integration for digital information processing, storage, transmission, and representation.
Advances in Multimedia maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.
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Chaotic Lightweight Cryptosystem for Image Encryption
Data transmission over the Internet and the personal network has been risen day by day due to the advancement of multimedia technology. Hence, it is today’s prime concern to protect the data from unauthorized access and encrypt the multimedia element as they are stored on the web servers and transmitted over the networks. Therefore, multimedia data encryption is essential. But, the multimedia encryption algorithm is complex to implement as it requires more time and memory space. For this reason, the lightweight image encryption algorithm gains popularity that requires less memory and less time along with low power or energy and provides supreme security for limited devices. In this study, we have studied the chaotic-based lightweight image encryption method. At first, we have presented a standard framework and algorithm based on two chaotic maps such as Arnold and logistic for lightweight image encryption and performed some experiments. We have analyzed different groups of images such as miscellaneous, medical, underwater, and texture. Experimentations have provided the largest entropy 7.9920 for medical image (chest X-ray), large key space 2m×m×8, and average encryption and decryption times are 3.9771 s and 3.1447 s, respectively. Besides, we have found an equal distribution of pixels and less correlation coefficients among adjacent pixels of the encrypted image. These criteria indicate an efficient image encryption method. Also, our method is efficient and less complex than the existing state-of-the-art methods.
Face Alignment Algorithm Based on an Improved Cascaded Convolutional Neural Network
Aiming at the problem of a large number of parameters and high time complexity caused by the current deep convolutional neural network models, an improved face alignment algorithm of a cascaded convolutional neural network (CCNN) is proposed from the network structure, random perturbation factor (shake), and data scale. The algorithm steps are as follows: 3 groups of lightweight CNNs are designed; the first group takes facial images with face frame as input, trains 3 CNNs in parallel, and weighted outputs the facial images with 5 facial key points (anchor points). Then, the anchor points and 2 different windows with a shake mechanism are used to crop out 10 partial images of human faces. The networks in the second group train 10 CNNs in parallel and every 2 networks’ weighted average and colocated a key point. Based on the second group of networks, the third group designed a smaller shake mechanism and windows, to achieve more fine-tuning. When training the network, the idea of parallel within groups and serial between groups is adopted. Experiments show that, on the LFPW face dataset, the improved CCNN in this paper is superior to any other algorithm of the same type in positioning speed, algorithm parameter amount, and test error.
Multimedia Archives: New Digital Filters to Correct Equalization Errors on Digitized Audio Tapes
Multimedia archives face the problem of obsolescing and degrading analogue media (e.g., speech and music recordings and video art). In response, researchers in the field have recently begun studying ad hoc tools for the preservation and access of historical analogue documents. This paper investigates the active preservation process of audio tape recordings, specifically focusing on possible means for compensating equalization errors introduced in the digitization process. If the accuracy of corrective equalization filters is validated, an archivist or musicologist would be able to experience the audio as a historically authentic document such that their listening experience would not require the recovery of the original analogue audio document or the redigitization of the audio. Thus, we conducted a MUSHRA-inspired perception test (n = 14) containing 6 excerpts of electronic music (3 stimuli recorded NAB and 3 recorded CCIR). Participants listened to 6 different equalization filters for each stimulus and rated them in terms of similarity. Filters included a correctly digitized “Reference,” an intentionally incorrect “Foil” filter, and a subsequent digital correction of the Foil filter that was produced with a MATLAB script. When stimuli were collapsed according to their filter type (NAB or CCIR), no significant differences were observed between the Reference and MATLAB correction filters. As such, the digital correction appears to be a promising method for compensation of equalization errors although future study is recommended, specifically containing an increased sample size and additional correction filters for comparison.
Knowledge Graph Reasoning Based on Tensor Decomposition and MHRP-Learning
In the process of learning and reasoning knowledge graph, the existing tensor decomposition technology only considers the direct relationship between entities in knowledge graph. However, it ignores the characteristics of the graph structure of knowledge graph. To solve this problem, a knowledge graph reasoning algorithm based on multihop relational paths learning (MHRP-learning) and tensor decomposition is proposed in this paper. Firstly, MHRP-learning is adopted to obtain the relationship path between entity pairs in the knowledge graph. Then, the tensor decomposition is performed to get a novel learning framework. Finally, experiments show that the proposed method achieves advanced results, and it is applicable to knowledge graph reasoning.
A Vehicle Reidentification Algorithm Based on Double-Channel Symmetrical CNN
It has become a challenging research topic to accurately identify the vehicles in the past from the mass monitoring data. The challenge is that the vehicle in the image has a large attitude, angle of view, light, and other changes, and these complex changes will seriously affect the vehicle recognition performance. In recent years, the convolutional neural network (CNN) has achieved great success in the field of vehicle reidentification. However, due to the small amount of vehicle annotation in the dataset of vehicle reidentification, the existing CNN model is not fully utilized in the training process, which affects the ability to identify the deep learning model. In order to solve the above problems, a double-channel symmetric CNN vehicle recognition algorithm is proposed by improving the network structure. In this method, two samples are taken as input at the same time, in which each sample has complementary characteristics. In this case, with limited training samples, the combination of inputs will be more diversified, and the training process of the CNN model will be more abundant. Experiments show that the recognition accuracy of the proposed algorithm is better than other existing methods, which further verifies the effectiveness of the proposed algorithm in this study.
Lifting-Based Fractional Wavelet Filter: Energy-Efficient DWT Architecture for Low-Cost Wearable Sensors
This paper proposes and evaluates the LFrWF, a novel lifting-based architecture to compute the discrete wavelet transform (DWT) of images using the fractional wavelet filter (FrWF). In order to reduce the memory requirement of the proposed architecture, only one image line is read into a buffer at a time. Aside from an LFrWF version with multipliers, i.e., the LFr, we develop a multiplier-less LFrWF version, i.e., the LFr, which reduces the critical path delay (CPD) to the delay of an adder. The proposed LFr and LFr architectures are compared in terms of the required adders, multipliers, memory, and critical path delay with state-of-the-art DWT architectures. Moreover, the proposed LFr and LFr architectures, along with the state-of-the-art FrWF architectures (with multipliers (Fr) and without multipliers (Fr)) are compared through implementation on the same FPGA board. The LFr requires 22% less look-up tables (LUT), 34% less flip-flops (FF), and 50% less compute cycles (CC) and consumes 65% less energy than the Fr. Also, the proposed LFr architecture requires 50% less CC and consumes 43% less energy than the Fr. Thus, the proposed LFr and LFr architectures appear suitable for computing the DWT of images on wearable sensors.