organizing rushes video by visually similar setting
Grouping shots recorded in front of the same or similar setting is a useful tool for organizing a collection of rushes video. We propose an approach that clusters key frames extracted from rushes by visual similarity of the setting shown in the shot. A region descriptor based on the covariance of selected features is used to estimate the dissimilarity of two key frames. The metric based on the generalized eigenvalues is used to determine the distance of two region covariance matrices. Integral images support efficient covariance computation of arbitrary rectangular regions. Based on the determined similarity of image regions, a dissimilarity matrix of all key frames is calculated. This dissimilarity matrix is used to build the clusters which reflect the different settings. The proposed algorithm is evaluated on a subset of the TRECVID BBC 2007 rushes data set.
clustering region covariance trecvid content-based indexing multidimensional scaling k-means setting detection