Discovering compact topical descriptors for web video retrieval
Describing videos efficiently is an important task for content based web video retrieval. To solve this problem, we propose an unsupervised approach based on an undirected topic model to learn a compact topical descriptor upon the bag-of-words (BoW) video representation. In our method, words in a BoW are assumed to have different topic features, and the topical descriptor of an entire video is obtained by aggregating those features, which makes the descriptor contain information about relative strength of topics. To improve the descriptor interpretability, an L1 penalty is used to control the topical sparsity. Furthermore, efficient learning and inference algorithms are presented. We evaluate the proposed descriptor on the Columbia Consumer Video dataset. Experimental results demonstrate that compared with the BoW and other topical representations, the proposed compact descriptor has better performance in web video retrieval.