Unsupervised anchor shot detection using multi-modal spectral clustering

This paper presents a novel unsupervised method for anchor shot detection using spectral clustering with multi-modal features. Unlike previous unsupervised studies where the acoustic trajectory features can not be combined with visual features directly, only a pairwise distance matrix from each attribute is needed instead of individual samples so that diverse information from heterogeneous features can be integrated in a unified manner. Experimental evaluation on a subset of the TRECVID 2004 dataset showed that an appropriate incorporation of the acoustic information with visual information will improve the F1 score from 0.68 for visual information only system to 0.87 in our unsupervised anchor shot detection system. Also a comparison study on the same dataset with a supervised system showed that the performance of our unsupervised system approach that of the supervised system.