A Hierarchical Scheme for Video‐Based Person Re‐identification Using Lightweight PCANet and Handcrafted LOMO Features

Chinese Journal of ElectronicsVolume 30, Issue 2 p. 289-295 Original Research PaperFree Access A Hierarchical Scheme for Video-Based Person Re-identification Using Lightweight PCANet and Handcrafted LOMO Features Li Youjiao, Corresponding Author liyoujiao@emails.bjut.edu.cn Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124 China Faculty of Information Technology, College of Micro-electronics, Beijing University of Technology, Beijing, 100124 China College of Computer Science and Technology, Shandong University of Technology, Zibo, 255000 ChinaSearch for more papers by this authorZhuo Li, Corresponding Author zhuoli@bjut.edu.cn Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124 China Faculty of Information Technology, College of Micro-electronics, Beijing University of Technology, Beijing, 100124 ChinaSearch for more papers by this authorLi Jiafeng, Corresponding Author lijiafeng@bjut.edu.cn Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124 China Faculty of Information Technology, College of Micro-electronics, Beijing University of Technology, Beijing, 100124 ChinaSearch for more papers by this authorZhang Jing, Corresponding Author zhj@bjut.edu.cn Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124 China Faculty of Information Technology, College of Micro-electronics, Beijing University of Technology, Beijing, 100124 ChinaSearch for more papers by this author Li Youjiao, Corresponding Author liyoujiao@emails.bjut.edu.cn Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124 China Faculty of Information Technology, College of Micro-electronics, Beijing University of Technology, Beijing, 100124 China College of Computer Science and Technology, Shandong University of Technology, Zibo, 255000 ChinaSearch for more papers by this authorZhuo Li, Corresponding Author zhuoli@bjut.edu.cn Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124 China Faculty of Information Technology, College of Micro-electronics, Beijing University of Technology, Beijing, 100124 ChinaSearch for more papers by this authorLi Jiafeng, Corresponding Author lijiafeng@bjut.edu.cn Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124 China Faculty of Information Technology, College of Micro-electronics, Beijing University of Technology, Beijing, 100124 ChinaSearch for more papers by this authorZhang Jing, Corresponding Author zhj@bjut.edu.cn Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124 China Faculty of Information Technology, College of Micro-electronics, Beijing University of Technology, Beijing, 100124 ChinaSearch for more papers by this author First published: 01 March 2021 https://doi.org/10.1049/cje.2021.02.001 This work is supported by the National Natural Science Foundation of China (No.61531006, No.61602018, and No.61701011), Beijing Municipal Natural Science Foundation Cooperation Beijing Education Committee (No.201910005007, No.KZ201810005002). AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract A two-level hierarchical scheme for video-based person re-identification (re-id) is presented, with the aim of learning a pedestrian appearance model through more complete walking cycle extraction. Specifically, given a video with consecutive frames, the objective of the first level is to detect the key frame with lightweight Convolutional neural network (CNN) of PCANet to reflect the summary of the video content. At the second level, on the basis of the detected key frame, the pedestrian walking cycle is extracted from the long video sequence. Moreover, local features of Local maximal occurrence (LOMO) of the walking cycle are extracted to represent the pedestrian' s appearance information. In contrast to the existing walking-cycle-based person re-id approaches, the proposed scheme relaxes the limit on step number for a walking cycle, thus making it flexible and less affected by noisy frames. Experiments are conducted on two benchmark datasets: PRID 2011 and iLIDS-VID. The experimental results demonstrate that our proposed scheme outperforms the six state-of-art video-based re-id methods, and is more robust to the severe video noises and variations in pose, lighting, and camera viewpoint. I. Introduction The goal of person re-identification (re-id) is to identify and associate a certain target person when he/she appears across non-overlapping camera views. Person re-id has been widely applied in various intelligent surveillance and public security systems, which has attracted the attention of researchers from both academic and industrial fields. However, person re-id is a challenging task due to the cluttered background and large variations of illumination, viewpoint, and pose. Basically, the main challenge in person re-id lies in the fact that intra-class variability is often significantly larger than inter-class variability. In the past few years, many efforts have been made in person re-id, with most of them based on still image[1]. However, in the image-based person re-id, only a few still images are contained for each person, which is inadequate to overcome the problems of occlusion, viewpoint and pose change, and generate a robust pedestrian representation. Very recently, some researchers consider performing person re-id by using video sequences[2, 3]. The use of videos for person re-id has several advantages over still images. First, the video inherently contains rich spatial-temporal information, which can facilitate to obtain the more robust appearance representation of the pedestrian. In addition, sequential frames in the video can provide a large number of samples, which makes it convenient to train machine-learning algorithms that lead to better re-id performance. Although more information can be utilized through videos, more challenges come along. For example, redundant information in videos may have an influence on the recognition result considerably, not to mention the distraction information such as video noise. Some sample frames captured from video sequences are illustrated in Fig.1. Fig. 1Open in figure viewerPowerPoint Instances of video noise So far, only a few video-based methods have been proposed and the problems mentioned above remain unsolved[4]. Therefore, in this paper, a two-level hierarchical scheme is proposed by jointly exploiting keyframe-detection and walking-cycle-extraction. First of all, a lightweight Convolutional neural network (CNN) of PCANet[5] is adopted to obtain the robust representation of the pedestrian, which takes video sequences of different views as input. At the first level, a novel key-frame-detection method based on the PCANet features is proposed. It aims to select the most discriminative frame from the entire video sequence as the key frame to reflect the fundamental content of the video. Second, at the second level, a pedestrian walking cycle is extracted based on the detected key frame. Then, local features of Local maximal occurrence (LOMO)[6] are extracted from the walking cycle. The feature of walking cycle conveys detailed information of person's appearance. Finally, the walking cycle feature is fused with the average pooling of LOMO features of a video to enhance the robustness of the pedestrian representation. To summarize the main contributions of this paper, first, a key-frame-detection method based on PCANet is proposed. Second, a walking cycle extraction algorithm is designed for video-based person re-id. In contrast to the walking-cycle-extraction methods based on one single step, the proposed method relaxes the limit on step number for a walking cycle, thus making it robust and flexible. II. Related work In general, most of the existing person re-id algorithms focus primarily on either feature representation or distance metric learning. As deep learning research gradually proceeds deeper since 2012[7-11], it is constantly applied to person re-id[12]. Deep network architectures make it possible to learn more hierarchical semantic information. Recently, the researchers have moved their attention to the video-based person re-id. The existing methods focus on feature extraction from a video sequence to represent the video content, which can be categorized into four classes: frame-aggregate-based methods[2], spatial-temporal-based methods[13], key-frame-based methods1[14] and walking-cycle-based methods[4]. In frame-aggregate-based methods, the hand-crafted or deep features are extracted from each video frame and then directly aggregated by ave/max pooling into a global feature vector. This kind of method is simple yet sensitive to video variation. Spatial-temporal-based methods refer to those that employ the spatial-temporal features of the video, which can reflect human motion information and are complementary to the appearance features of the person. However, spatial-temporal features are sensitive to viewpoint, scale, and speed. However, it should be noted that even if massive video data are available, the information that people are really interested in might be quite a few. Therefore, many researchers focus on exploring key information from videos. Key-frame-based methods generally select the most discriminative frame from video sequences to represent the entire video. For the last walking-cycle-based methods, several walking cycles are detected from a video sequence as accurately as possible. Due to the symmetry of the walking action, a complete cycle should contain two continuous steps from each leg. However, because it is difficult to distinguish between the two steps, each detected walking cycle contains only one single step. Every walking cycle contains several video frames, thus can provide more information compared to the key-frame-based methods. However, persons in videos walk at various speed, in different directions, and are often in crowds, so it is quite difficult and computationally demanding to exactly extract many walking cycles. In addition, frames between different walking cycles are abandoned, which may lead to the loss of useful information such as view-invariant features. Therefore, walking-cycle-extraction methods which are more robust and effective are required. Unlike the previous work, this paper focuses on generating a more complete and representative walking-cycle-extraction method. More specifically, based on the detected key frame, we extract two continuous steps in both the forward and backward directions to form a walking cycle. III. Hierarchical Scheme for Video-based Person Re-identification The framework of the proposed two-level hierarchical scheme for video-based person re-id is illustrated in Fig. 2. In the first level of the proposed method, the entire frame of an input video passes through PCANet, and the deep features of each frame are obtained. The set of features are then aggregated by average pooling to generate a deep feature vector. Next, the frame with the feature most similar to the deep feature vector is selected as the key frame. This frame can reflect the most reprehensive and stable information for the entire video. In the second level, the position of the detected key frame is taken as the starting point. In both the forward and backward directions, the scheme determines the positions of two frames which are most similar to the key frame. The distance between the two positions is regarded as a walking cycle. Then frame-level LOMO features within the walking cycle are aggregated by max pooling to generate a feature vector. Next, the walking-cycle feature is fused with the average pooling of LOMO features for a video sequence to form the person representations. Finally, the distance metric learning method is adopted to learn a feature space for the representations. Fig. 2Open in figure viewerPowerPoint The framework of the proposed two-level scheme 1. Extraction of key frame feature The intention of this phase is to extract the key frame of the video to represent the main content of the video. Compared with many computer vision tasks, there contain much fewer samples for each class in the datasets for person re-id, which makes over-fitting a common problem in this field. Consequently, PCANet is utilized as the feature extractor and key-frame detector in this work. It is a simple and effective deep learning architecture with its convolution filter banks chosen from PCA filter. Due to its limited parameters, PCANet can avoid over-fitting effectively[15]. The basic architecture of PCANet is shown in Figure 3. It is composed of three steps: the first two stage are PCA filter convolutions and the last stage is hashing and histogram generation. The frame mean was subtracted before convolution. Fig. 3Open in figure viewerPowerPoint The structure of the PCANet The model parameters of PCANet include frame size m and n, patch size k1 and k2, overlap ratio, layer number, and filter number L1 and L2. In our experiments, the frame size is set to 128×64, the patch size 10×10, the overlap ratio 0.5, the layer number 3, and the L1 and L2 are 30 and 8 respectively. Consequently, through the two-stage PCANet, a 161280-dimensional feature vector is generated for an input image in the proposed scheme. For computational simplification, the dimension of the deep feature is further reduced to 128-dimensional by PCA operation. The extracted features from PCANet are then used to detect the key frame. The detailed descriptions of the detection are presented as follows. Let F = {f1, f2, …, fT} be a video sequence of length T, where fi is the ith frame. For all frames in F, deep features are aggregated by average pooling operation to generate a feature vector, denoted as: F P C A N e t = 1 T ∑ i = 1 T P C A N e t ( i ) ( 1 ) (1) where PCANet(i) is the PCANet deep feature of ith frame. Then the similarity measure of distances between PCANet(i) and FPCANet is computed. The frame corresponding to the minimum distance is taken as the key frame of the video, as given in Algorithm 1. Algorithm 1 Key-frame detection Input: FPCANet of video F, video length T, all PCANet(i) ∈ F; for i = 1 to T do Calculate the difference D(i) between PCANet(i) and FPCANet; Determine the location of frame j* whose D(j*) is minimal; end for Output j* Examples of the key-frame detection results of iLIDS-VID[14] are listed in Fig.4. It shows that there are little occlusion and clutter in the detected key frames. This is because the pooling operation can suppress the disturb of partial occlusion and deformation effectively, which makes the algorithm more robust. Fig. 4Open in figure viewerPowerPoint Examples of the key-frame detection results from different videos 2. Extraction of walking cycle feature The action of walking pedestrian changes periodically, and thus, an entire walking cycle will cover variations of appearance and pose for a pedestrian during walking. Once a walking cycle is extracted accurately, the comprehensive representation of pedestrian appearance and different action primitive can be obtained. A complete walking cycle contains two continuous steps from each leg. The goal of this phase is to extract the two steps based on the periodicity of walking action. Frame similarity is used to find the boundaries of a walking cycle. Specifically, this work takes the position of the detected key frame as the starting point. In both of the forward and backward directions, the proposed scheme determines the positions of two frames which are mostly similar to the key frame. The frames between the two positions are regarded as a walking cycle. In this paper, LOMO is adopted to measure the similarity between two frames. LOMO is a local feature specifically crafted for person re-id and robust to the variations in lighting and viewpoint. The spatial information contained in LOMO can reflect the characteristics of different body part, which makes LOMO capable of measuring the distance between different video frames. In detail, Let S be a dataset including the videos of N person, and Pj the jth person video sequence. Pj∈S. T is the length of Pj. Suppose the key-frame position in Pj is j*, then an entire walking cycle could be denoted as: C ∈ { t l e f t , . . . , j * , . . . , t r i g h t } ( 2 ) (2) where tleft and tright are the positions of two frames which are mostly similar to the key frame in both the forward and backward directions, which will be treated as the left and right boundary of the walking cycle for Pj. The detailed descriptions for the detection of tleft are as follows. First of all, LOMO features of each frame in Pj are extracted, denoted as g = {g1, g2, …, gT}, in which g(i) is the LOMO feature of the ith frame. Then the feature distances between g and g(j*) are computed. Let D(i) be the feature distance between the ith frame and the extracted key frame. Function curve of D(i) is shown in the upper part of Fig.5. The 7th bounding box indicates the key frame. X-axis reflects the number of frames, and Y-axis is the value of D(i). When i is equal to j*, D(i) attains its minimum. We observed that the person's two legs are the farthest away in the key frame of Fig.5. Besides, the local maximum of D(i) corresponds to the frame when the person's two legs overlap while at the local minimum the two legs are the farthest away again, just like the key frame. Hence, when D(i) passes the second critical point in the backward direction, the distance between g(j*) and the LOMO feature at this point attains a minimum. So we take the position at this point as the left boundary tleft of a walking cycle. In addition, it is likely that j* may be very close to the beginning or ending point of the video. To prevent the beyond-boundary issue, the function of walking-cycle detection is formulated as follows: R ( P j ) = [ B l e f t ( P j ) , B r i g h t ( P j ) ] = [ 1 , t r i g h t ] , t l e f t < 1 [ t l e f t , T ] , t r i g h t > T [ t l e f t , t r i g h t ] , otherwise ( 3 ) (3) where Bleft(Pj) and Bright(Pj) are the functions of walking-cycle boundary detection respectively. For the faster location to tleft, the Newton method is adopted to get a solution to the local minimum for the above optimal question. The detailed process is as follows. Algorithm 2 Walking-cycle extraction 1: Initialization: Given key-frame location j*. Let ε=1.0e-7 and i = j*–1. 2: Iterate Update: i = i – D′(i)/D″(i) 3: if D′(i) > ε, go to Step 2. 4: Output i // solution of maxi D(i) 5: Iterate Update: i = i – D′(i)/D″(i) 6:if D′(i) > ε, go to Step 5. 7: Output i // solution of mini D(i) The output of the algorithm is the tleft for a walking cycle. Similarly, the right boundary tright can be obtained. Fig. 5Open in figure viewerPowerPoint Illustration of walking-cycle extraction In the lower part of Fig.5, the extraction result of the walking cycle for pedestrian 102 under cam1 in PRID 2011 dataset[16] is demonstrated. Take the position of the key-frame j* as the starting point, and the proposed scheme first find two positions of the local maximum in both the forward and backward directions. Frames at the two positions are indicated by a blue bounding box. The pedestrian appearance difference between the key frame and the two frames attains a local maximum. Then, the local minimum points along the same direction as the walking cycle boundary are found, indicated by a yellow bounding box. The appearance difference between the key-frame and frames at the two positions attains a local minimum. After the walking cycle is extracted, LOMO features of frames within it are extracted. Max pooling is performed on the set of features to obtain a feature vector, which can represent the characteristics of a whole walking cycle well. While LOMO feature is extracted in this work, the patch size and overlap radio are set to 10×10 and 0.5 respectively. As to the rest of the parameters, like the bin size of the texture and color histogram, the default setting of LOMO is adopted. Accordingly, a 26960-dimensional feature vector can be generated. Suppose all the LOMO features in C are combined to form a feature set g* = {g(tleft), …, g(tright)}(g* refers to the features extracted from each frame within a walking cycle)), the features in g* are then aggregated by max pooling to obtain a feature vector, namely fW. f W = max ( g * ) ( 4 ) (4) The purpose of the max pooling operation is to generate a feature that is robust to small distortions. Fig. 6 shows an example of walking-cycle extraction result by using the proposed scheme. It can be seen that the extracted walking cycle contains not only rich appearance information but also entire pedestrian pose information. Fig. 6Open in figure viewerPowerPoint Examples of the walking-cycle extraction results. (a) we demonstrate the video sequence of pedestrian 007 under cam2 in iLIDS-VID dataset; (b) we demonstrate the extraction result of the walking cycle At last, in order to further improve the identification performance, the walking-cycle feature is fused with the average pooling of LOMO features for a person video sequence. The pedestrian descriptor is then written as: f = [ α f W , ( 1 - α ) f A V E ] ( 5 ) (5) where fAVE is the average pooling of LOMO features and is the weight for the two descriptors. We define the descriptor weight as follows: α = d W / ( d W + d A V E ) ( 6 ) (6) where dW and dAVE are the distance measure score of fW and fAVE, respectively. IV. Experimental Results and Analysis To evaluate the proposed hierarchical scheme, extensive experiments have been conducted on two public benchmark datasets for video-based person re-id, the PRID 2011 dataset and the iLIDS-VID dataset. The PRID 2011 consists of video pairs recorded from two static surveillance cameras. It was captured in spacious outdoor scenes with a clean background and rare occlusions, and the variations of the pedestrian pose are small. 385 pedestrians were recorded in camera A, and 749 pedestrians in camera B. Among all pedestrians, only 200 pedestrians were recorded in both cameras. Each video is comprised of 5 to 675 frames, with an average of 100. Examples of PRID 2011 are shown in Fig.7(a). To guarantee the effective length of the video, we select 178 pedestrians with more than 27 frames. The iLIDS-VID dataset also uses two static surveillance cameras. It was captured in an airport arrival hall under a CCTV network. Examples of iLIDS-VID are shown in Fig.7(b). iLIDS-VID contains 600 videos of 300 persons. Each video is comprised of 23 to 192 frames, with an average of 73. The challenges of this dataset lie in lighting and viewpoint variations, clothing similarities, complicated background, and occlusions. Compared with the PRID 2011, iLIDS-VID is more complicated and challenging. Fig. 7Open in figure viewerPowerPoint Sample frames in PRID 2011 and iLIDS-VID datasets We evaluate the proposed descriptor with a distance metric learning method, Top-push Distance Learning (TDL)[13]. TDL is specifically proposed for video-based person re-id. It poses a more stringent constraint on top-rank matching, so as to make the method more effective. Specifically, TDL is not the distance between positive sample pairs and all relative negative sample pairs, but the minimum distance. Our experiments followed the evaluation protocol in Ref.[13] for the PRID 2011 and iLIDS-VID datasets. Both datasets are randomly divided into training set and testing set by half. Pedestrian videos from one view were matched with those from another view, and the rank of the correct match was obtained. The expectations of the correct match at rank k are computed as rank-k matching rates. The procedure is repeated 10 times and the average results of expectation are reported. We compare the performance of the proposed scheme with six state-of-the-art video-based person re-id methods, including DFGP[2], Video ranking[14], Spatiotemporal fisher vector (STFV)[4], Recurrent convolutional network (RCN)[12], Robust AnChor embedding (RACE)[17], and Multi-view coupled dictionary learning (MCDL)[18]. DFGP cascaded aggregated PCN deep feature and LOMO feature as the pedestrian descriptor, and combined TDL to perform the identification. Video ranking is a keyframe-based method. Moreover, video ranking utilize the spatial-temporal information in videos to achieve better performance. STFV is the baseline method of walking-cycle-based person re-id. RCN integrated recurrent neural network and convolutional neural network in an end-to-end manner. Similarly, RACE is a deep-learning-based method. Through a learned multi-view-based coupled dictionary, MCDL can fuse multiple categories of features such as color and texture. The results of the comparison between the proposed method and the six state-of-the-art video-based person re-id methods are reported in Table 1 and 2 respectively. The experimental results show that the performance of the proposed method on both datasets is better than other methods in general. Compared with the baseline method of STFV, our method improves the average matching rate by 11%. Especially on Rank-1, the average matching rate increased by 18%. The above results indicate that our walking-cycle learning method could well approximate human perception and capture representative walking cycles from pedestrian videos. As a result, the pedestrian descriptor is more robust, which makes the proposed method effectively eliminate the interference of complicated environments and quickly locate the right matching result. In addition, our method performed much better on PRID 2011 than on iLIDS-VID. A possible reason lies in the similar pedestrian motion and minor motion difference in PRID 2011. Table 1. Comparison Results on PRID 2011 dataset Methods Rank – 1 Rank – 5 Rank – 10 Rank –20 MCDL 27.6 59.5 74.7 89.2 Video ranking 37.6 63.9 75.3 88.3 RACE 50.6 79.4 88.6 91.8 DFGP 51.6 83.1 91.0 95.5 STFV 64.1 87.3 89.9 92.0 RCN 70 90 95 97 Our method 80.9 95.7 98.3 99.7 We also evaluated the performance of using walking-cycle features and average pooling features separately, as is reported in Table 3. Walking cycle and average pooling correspond to using walking-cycle or average pooling features alone, respectively. Our method fuses the two kinds of features together. It shows that using the two kinds of features alone can also exceed many previous works, and the fusion of the two kinds of features can definitely obtain a better result. The reason is that the detected walking cycles are legible and representative, which could provide valuable information in identification. Table 2. Comparison Results on iLIDS-VID dataset Methods Rank – 1 Rank – 5 Rank – 10 Rank – 20 RACE 19.3 39.3 53.3 68.7 Video ranking 34.5 56.3 67.5 77.5 DFGP 34.5 63.3 74.5 84.4 MCDL 26.1 53.4 69.7 86.1 STFV 44.3 71.7 83.7 91.7 RCN 58 84 91 96 Our method 63.5 84.1 91.3 96.6 For a fair comparison, we further list the results of the combination of the walking-cycle-based baseline method STFV and the LOMO features, as shown in Table 3. For each walking cycle extracted from the videos, STFV divided it into 24 body-action units. In each unit, dense local and spatio-temporal features are extracted to generate a 37632-dimensional video representation. Here we only retain the walking-cycle detection results and replace the original STFV features with the 26960-dimensional LOMO features. The method above is denoted as STFV + LOMO. Compared with STFV + LOMO, the proposed method improves the average accuracy by 1.2%. This shows that the complete walking cycle contains long-time appearance difference during different phases of the walking action, which is robust to view variances. Table 3. Different method results on iLIDS-VID Features Rank – 1 Rank – 5 Rank – 10 Rank – 20 STFV + LOMO 31.7 60.6 73.6 83.1 Walking cycle 32.0 62.1 75.2 85.7 Average pooling 62.2 83.8 91.3 96.4 Our method 63.5 84.1 91.3 96.6 V. Conclusions and Discussion In this paper, we proposed a hierarchical scheme to address the video-based person re-identification problem. Despite more abundant video information than still-image based representation, the pedestrian in a video displays high intra-class variance. Therefore, this paper introduces a walking-cycle-extraction method to learn a principal appearance model. Experimental results were evaluated on two benchmark datasets including PRID 2011 and iLIDS-VID. Compared with many state-of-the-art methods, our method has achieved better performance. As to future work, there is still space to improve if more temporal-spatial features of videos are utilized. Biographies LI Youjiao is a Ph.D. candidate at the Faculty of Information Technology, Beijing University of Technology. He is now a lecturer of Shandong University of Technology. His research interest covers computer vision and deep learning. (Email: liyoujiao@emails.bjut.edu.cn) ZHUO Li (corresponding author) is a professor at Beijing University of Technology. She received her bachelor degree in radio technology from the University of Electronic Science and Technology in 1992, master degree in signal & information processing from the Southeast University in 1998, and Ph.D. degree in pattern recognition and intellectual system from Beijing University of Technology in 2004. Her research interest covers image/video coding and transmission, multimedia content analysis, and multimedia information security. Corresponding author of this paper. (Email: zhuoli@bjut.edu.cn) LI Jiafeng is a assistant professor at Signal & Information Processing Laboratory, Beijing University of Technology. He was in the Department of Neurosurgery, University of Pittsburgh as a visiting scholar from 2014 to 2015. His research interest covers computer vision, image enhancement, and image restoration. (Email: lijiafeng@bjut.edu.cn) ZHANG Jing received her Ph.D. degree from Beijing University of Technology in 2008. She is a professor at the Beijing University of Technology. She is also a research scholar in the Department of Computer Science, the University of Texas at San Antonio. She is the author of more than 50 journal papers and has written three book chapters. Her current research interests include image processing, image recognition, and image retrieval. 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