Fast Search for MPEG Video Clips Using Adjacent Pixel Intensity Difference Quantization Histogram Feature

In this paper, we propose a novel fast search algorithm for short MPEG video clips from video database. This algorithm is based on the adjacent pixel intensity difference quantization (APIDQ) algorithm, which had been reliably applied to human face recognition previously. An APIDQ histogram is utilized as the feature vector of the frame image. Instead of fully decompressed video frames, partially decoded data, namely DC images are utilized. Combined with active search [4], a temporal pruning algorithm, fast and robust video search can be realized. The proposed search algorithm has been evaluated by 6 hours of video to search for given 200 MPEG video clips which each length is 15 seconds. Experimental results show the proposed algorithm can detect the similar video clip in merely 80ms, and Equal Error Rate (ERR) of 3 % is achieved, which is more accurately and robust than conventional fast video search algorithm. Keywords—Fast search, Adjacent pixel intensity difference quantization (APIDQ), DC image, Histogram feature.

[1]  Kotani Koji,et al.  Face Recognition based on the Adjacent Pixel Intensity Difference Quantization , 2005 .

[2]  Kunio Kashino,et al.  A quick AND/OR search for multimedia signals based on histogram features , 2003 .

[3]  Wolfgang Effelsberg,et al.  On the detection and recognition of television commercials , 1997, Proceedings of IEEE International Conference on Multimedia Computing and Systems.

[4]  Boon-Lock Yeo,et al.  Rapid scene analysis on compressed video , 1995, IEEE Trans. Circuits Syst. Video Technol..

[5]  Ruud M. Bolle,et al.  Comparison of sequence matching techniques for video copy detection , 2001, IS&T/SPIE Electronic Imaging.

[6]  Rakesh Mohan,et al.  Video sequence matching , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[7]  Avideh Zakhor,et al.  Efficient video similarity measurement with video signature , 2002, Proceedings. International Conference on Image Processing.

[8]  J. David Schaffer,et al.  Evolvable visual commercial detector , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[9]  Hiroshi Murase,et al.  Focused color intersection with efficient searching for object extraction , 1997, Pattern Recognit..