As digital libraries and video databases grow, we need methods to assist us in the synthesis and analysis of digital video. Since the information in video databases can be measured in thousands of gigabytes of uncompressed data, tools for efficient summarizing and indexing of video sequences are indispensable. In this paper, we present a method for effective classification of different types of videos that makes use of video summarization that is the form of a storyboard of keyframes. To produce the summarization, we first generate a universal basis on which to project a video frame that effectively reduces any video to the same lighting conditions. Each frame is represented by a compressed chromaticity signature. We then set out a multi-stage hierarchical clustering method to efficiently summarize a video. Finally we classify TV videos using a trained hidden Markov model on the compressed chromaticity signatures and also temporal features of videos that are represented by their summaries.
Motivated by colour constancy work in physics-based vision, we develop a new low-dimensional video frame feature that is effectively insensitive to lighting change and apply the feature to keyframe production using hierarchical clustering. The new image feature results from normalising colour channels for frames and then treating 2D histograms of chromaticity as images and compressing these. Because we effectively reduce any video to the same lighting conditions, we can precompute a universal basis on which to project video frame feature vectors. The new feature thus has the advantage of more expressively capturing essential colour information, and is useful for video indexing because it is very low-dimension -the feature vector is only of length 8. We carry out clustering efficiently by adapting the 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, compared to a previous well-known technique, results are excellent.
As digital libraries and video databases grow, we need methods to assist us in the synthesis and analysis of digital video. Since the information in video databases can be measured in thousands of gigabytes of uncompressed data, tools for efficient summarizing and indexing of video sequences are indispensable. In this paper, we present a method for effective classification of different types of videos that makes use of video summarization that is the form of a storyboard of keyframes. To produce the summarization, we first generate a universal basis on which to project a video frame that effectively reduces any video to the same lighting conditions. Each frame is represented by a compressed chromaticity signature. We then set out a multi-stage hierarchical clustering method to efficiently summarize a video. Finally we classify TV videos using a trained hidden Markov model on the compressed chromaticity signatures and also temporal features of videos that are represented by their summaries.
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