Mark S. Drew
ABSTRACT
We develop a new low-dimensional video frame feature that is more insensitive to lighting change, motivated by color constancy work in physics-based vision, and apply the feature to keyframe production using hierarchical clustering. The new feature has the further advantage of more expressively capturing image information and as a result produces a very succinct set of keyframes for any video. Because we effectively reduce any video to the same lighting conditions, we can produce a universal basis on which to project video frame features. We carry out clustering efficiently by adapting a hierarchical clustering data structure to temporally-ordered clusters. Using a new multi-stage hierarchical clustering method, we merge clusters based on the ratio of cluster variance to variance of the parent node, merging only adjacent clusters, and then follow with a second round of clustering. The second stage merges clusters incorrectly split in the first round by the greedy hierarchical algorithm, and as well merges non-adjacent clusters to fuse near-repeat shots. The new summarization method produces a very succinct set of keyframes for videos, and results are excellent.
- 1.B.-L. Yeo and M.M. Yeung. Classification, simplification and dynamic visualization of scene transition graphs for video browsing. In SPIE Storage and Retrieval for Image and Video Databases VI, 1998.Google Scholar
- 2.M.M. Yeung and B. Liu. Efficient matching and clustering of video shots. In ICIP '95, pages 338-341,1995. Google ScholarDigital Library
- 3.D. Zhong, H. Zhang, and S.-F. Chang. Clustering methods for video browsing and annotation. In SPIE Storage and Retrieval for Image and Video Databases IV, pages 239-246,1996.Google ScholarCross Ref
- 4.J. R. Kender and B. L. Yeo. Video scene segmentation via continuous video coherence. In CVPR '98, pages 367-373,1998. Google ScholarDigital Library
- 5.A.M. Ferman and A.M. Tekalp. Efficient filtering and clustering methods for temporal video segmentation and visual summarization. J. Vis. Commun. & lmage Rep., 9:336-351, 1998.Google Scholar
- 6.A.M. Ferman and A.M. Tekalp. Multiscale content extraction and representation for video indexing. In SPIE Multimedia Storage and Archiving Systems 11, 1997.Google Scholar
- 7.H.J. Zhang, S.Y. Tan, S.W. Smoliar, and Y. Gong. Video parsing, retrieval and browsing: An integrated and content-based solution. In ACM Multimedia "95, pages 15-24, 1995. Google ScholarDigital Library
- 8.A. Hanjalic, M. Ceccarelli, R.L. Lagendijk, and J. Biemond. Automation of systems enabling search on stored video data. In SPIE Storage and Retrieval for Image and Video Databases V, pages 427--438,1997.Google ScholarCross Ref
- 9.D. DeMenthon, V. Kobla, and D. Doermann. Video summarization by curve simplification. In ACM MM98, 1998. Google ScholarDigital Library
- 10.M.S. Drew, J. Wei, and Z.N. Li. lllumination-invariant color object recognition via compressed chromaticity histograms of color-channel-normalized images. In ICCV98, pages 533-540. IEEE, 1998. Google ScholarDigital Library
- 11.J. Wei, M.S. Drew, and Z.N. Li. Illumination invariant video segmentation by hierarchical robust thresholding. In Electronic Imaging 198: Storage and Retrieval for Image and Video Databases I/1, pages 188-201. SPIE Vol. 3312, 1998.Google Scholar
- 12.G.D. Finlayson, P.M. Hubel, and S. Hordley. Colour by correlation. In Fifth Color Imaging Conf., pages 6-11, 1997.Google Scholar
- 13.E. Sahouria and A. Zakhor. Content analysis of video using principal components. 1EEE Trans. Circ. Sys. Vid. Tech., 9:1290-1298, 1999. Google ScholarDigital Library
- 14.A. Girgensohnand J. Boreczky.Time-constrained key frame selection technique. In IEEE MM Sys., pages 756-761,1999.Google Scholar
- 15.M. S. Drew, J. Wei, and Z.N. Li. Illumination-invariant image retrieval and video segmentation. Pattern Recognition, 32:1369-1388, 1999.Google ScholarCross Ref
- 16.C.E Borges. Trichromatic approximation method for surface illumination. J. Opt. Soc. Am. A, 8:1319-1323,1991.Google ScholarCross Ref
- 17.Mark S. Drew, Ze-Nian Li., and Xiang Zhong. Video dissolve and wipe detection via spatio-temporal images of chromatic histogram differences. In 1CIP'O0, 2000. To appear.Google Scholar
Index Terms
- Video keyframe production by efficient clustering of compressed chromaticity signatures (poster session)
Recommendations
Content-Based Keyframe Clustering Using Near Duplicate Keyframe Identification
In this paper, the authors propose an effective content-based clustering method for keyframes of news video stories using the Near Duplicate Keyframe NDK identification concept. Initially, the authors investigate the near-duplicate relationship, as a ...
An efficient hybrid clustering algorithm for molecular sequences classification
ACM-SE 44: Proceedings of the 44th annual Southeast regional conferenceThe k-means clustering and hierarchical agglomerative clustering algorithms are two popular methods to partition data into groups. The k-means clustering algorithm heavily favors spherical clusters and does not deal with noise adequately. To overcome ...
Far efficient K-means clustering algorithm
ICACCI '12: Proceedings of the International Conference on Advances in Computing, Communications and InformaticsClustering in data analysis means data with similar features are grouped together within a particular valid cluster. Each cluster consists of data that are more similar among themselves and dissimilar to data of other clusters. Clustering can be viewed ...
Comments