Fast Search Method for Large Video Database Using Histogram Features and Temporal Division
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:55307", author = "Feifei Lee and Qiu Chen and Koji Kotani and Tadahiro Ohmi", title = "Fast Search Method for Large Video Database Using Histogram Features and Temporal Division", 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.", volume = "4", number = "9", pages = "1411-4", }