Fast Search Method for Large Video Database Using Histogram Features and Temporal Division

Authors: Feifei Lee, Qiu Chen, Koji Kotani, Tadahiro Ohmi

Abstract:

In this paper, we propose an improved fast search algorithm using combined histogram features and temporal division method for short MPEG video clips from large video database. There are two types of histogram features used to generate more robust features. The first one 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. Another one is ordinal feature which is robust to color distortion. 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 30 seconds. Experimental results show the proposed algorithm can detect the similar video clip in merely 120ms, and Equal Error Rate (ERR) of 1% is achieved, which is more accurately and robust than conventional fast video search algorithm.

Keywords: Fast search, Adjacent pixel intensity differencequantization (APIDQ), DC image, Histogram feature.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1062504

References:

[1] K. Kashino, T. Kurozumi, and H. Murase, "Quick AND/OR search for multimedia signals based on histogram features", IEICE Trans., J83-D-II, vol.12, 2000, pp. 2735-2744.
[2] S.S. Cheung and A. Zakhor, "Efficient video similarity measurement with video signature", IEEE Trans. on Circuits and System for Video Technology, vol.13, no.1, 2003, pp. 59-74.
[3] A. Hampapur, K. Hyun, and R. Bolle, "Comparison of sequence matching techniques for video copy detection", SPIE. Storage and Retrieval for Media Databases 2002, 4676, San Jose, CA, USA, 2002, pp. 194-201.
[4] V.V. Vinod, H. Murase, "Focused color intersection with efficient searching for object extraction", Pattern Recognition, vol. 30, no.10, 1997, pp. 1787-1797.
[5] K. Kotani, F. Lee, Q. Chen, and T. Ohmi, "Face recognition based on the adjacent pixel intensity difference quantization histogram method", Proc. 2003 Int. Symposium on Intelligent Signal Processing and Communication Systems, D7-4, Japan, 2003, pp. 877-880.
[6] AT&T Laboratories Cambridge, The Database of Faces, at http://www.cl. cam.ac.uk/research/dtg/attarchive/facedatabase.html.
[7] L. Agnihotre, N. Dimitrova, T. McGee, S. Jeannin, S. Schaffer, J. Nesvadba, "Evolvable visual commercial detector", IEEE. International Conference on Computer Vision and Pattern Recognition, vol. 2, 2003, pp. 79-84.
[8] R. Lienhart, C. Kuhmunch, W. Effelsberg, "On the detection and recognition of television commercials", In Proc. IEEE Conf. on Multimedia Computing and Systems, 1997, pp. 509-516.
[9] R. Mohan, "Video sequence matching", In Proc. of the International Conference on Audio, Speech and Signal Processing, vol.6, 1998, pp. 3679-3700.
[10] B. Yeo and B. Liu, "Rapid scene analysis on compressed videos", IEEE Trans. on Circuits and Systems for Video Technology, vol.5, no.6, 1995, pp. 533-544.
[11] J. Yuan, L. Duan, Q. Tian, C. Xu, "Fast and Robust Short Video Clip Search Using an Index Structure", 6th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp.61-68, Oct., 2004.
[12] F. Lee, K. Kotani, Q. Chen, T. Ohmi, "Fast Search for MPEG Video Clips Using Adjacent Pixel Intensity Difference Quantization Histogram Feature," Proc. of the Int-l Conf. on Image and Vision Computing (ICIVC 2009), pp. 777-780, Paris, Jun. 2009.