A Rapid and Robust Method for Shot Boundary Detection and Classification in Uncompressed MPEG Video Sequences
Shot boundary and classification is the first and most important step for further analysis of video content. Shot transitions include abrupt changes and gradual changes. A rapid and robust method for shot boundary detection and classification in MPEG compressed sequences is proposed in this paper. We firstly only decode I frames partly in video sequences to generate DC images and then calculate the difference values of histogram of these DC images in order to detect roughly the shot boundary. Then, for abrupt change detection, shot boundary is precisely located by movement information of B frames. Shot gradual change is located by difference values of successive N I frames and classified by the alteration of the number of intra coding macroblocks (MBs) in P frames. All features such as the number of MBs in frames are extracted from uncompressed video sequences. Experiments have been done on the standard TRECVid video database and others to reveal the performance of the proposed method.
Keywords: Shot Boundary Detection, Classification, Abrupt Change, Gradual Change, MBs
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ABOUT THE AUTHORS
Chunmei Ma
Chunmei Ma received her M.S. degree in Content-Based Multimedia Information Retrieval from Nanjing University of Posts and Telecommunications, Nanjing, China, in 2007. She is currently a lecturer in the School of Computer Science, Qufu Normal University, Shandong, China. Her current research interests include shot boundary detection, image process and database technology.
Jiguo Yu
Jiguo Yu received his Ph.D. degree in operational research and control theory from Shandong University, Shandong, China, in 2004. From 2007, he has been a full professor in the School of Computer Science, Qufu Normal University, Shandong, China. His main research interests include wireless networks, algorithms, peer-to-peer computing and graph theory. In particular, he is interested in designing and analyzing algorithms for many computationally hard problems in computer networks. He is a member of the IEEE, and a senior member of the CCF (China Computer Federation).
Baogui Huang
Baogui Huang received his M.S. degree in Digital Image Process from Qufu Normal University, Shandong, China, in 2008. He is currently a lecturer in the School of Computer Science, Qufu Normal University, Shandong, China. His current research interests include image process and pattern recognition.
Chunmei Ma
Chunmei Ma received her M.S. degree in Content-Based Multimedia Information Retrieval from Nanjing University of Posts and Telecommunications, Nanjing, China, in 2007. She is currently a lecturer in the School of Computer Science, Qufu Normal University, Shandong, China. Her current research interests include shot boundary detection, image process and database technology.
Jiguo Yu
Jiguo Yu received his Ph.D. degree in operational research and control theory from Shandong University, Shandong, China, in 2004. From 2007, he has been a full professor in the School of Computer Science, Qufu Normal University, Shandong, China. His main research interests include wireless networks, algorithms, peer-to-peer computing and graph theory. In particular, he is interested in designing and analyzing algorithms for many computationally hard problems in computer networks. He is a member of the IEEE, and a senior member of the CCF (China Computer Federation).
Baogui Huang
Baogui Huang received his M.S. degree in Digital Image Process from Qufu Normal University, Shandong, China, in 2008. He is currently a lecturer in the School of Computer Science, Qufu Normal University, Shandong, China. His current research interests include image process and pattern recognition.
