Video keyframe production by efficient clustering of compressed chromaticity signatures (poster session)

  • Authors:

  • Mark S. Drew;James Au

  • Affiliations:

  • School of Computing Science, Simon Fraser University, Vancouver, B.C. Canada V5A 1S6;School of Computing Science, Simon Fraser University, Vancouver, B.C. Canada V5A 1S6

  • Venue:
  • MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
  • Year:
  • 2000

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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.