Spatiogram-Based Shot Distances for Video Retrieval.
Adrian Ulges, Christoph H. Lampert, Daniel Keysers · 2006
Abstract. We propose a video retrieval framework based on a novel combination of spatiograms and the Jensen-Shannon divergence, and validate its performance in two quantitative experiments on TRECVID BBC Rushes data. In the first experiment, color-based methods are tested by grouping redundant shots in an unsupervised clustering. Results of the second experiment show that motion-based spatiograms make a promising fast, compressed-domain descriptor for the detection of interview scenes. Experiment 1: Clustering Run-ID concept NN- error rate (%) CH-L1 baseline: color histograms 33.5 53.0 CS1D-JSD our framework 13.4 42.3 CS-JSD our framework 15.4 46.5 CS1D-PROB different similarity measure [1] 36.2 90.6 CS-PROB different similarity measure [1] 44.2 87.2 WH-L1 color histograms over local windows 16.1 50.3 BVW-Harris ”bag-of-visual-words ” 24.8 91.9 clustering- error (%)