Weakly supervised topic grouping of YouTube search results

Recent years have witnessed an explosive growth of user contributed videos on websites like YouTube and Metacafe, which usually provide a query-by-keyword functionality to facilitate the user browsing. For a given query, the returned videos typically contain multiple topics that are mixed up to duplicate the user browsing. Therefore, their diversification and grouping are highly demanded to improve the user experiences. However, the tagging and content qualities of user contributed videos are uncontrolled against their precise grouping. In this paper, we present a weakly supervised topic grouping paradigm to diversify the returned videos of a given keyword query. Our grouping is based on the bag-of-words visual signature quantized over the spatiotemporal STIP descriptor [1] extracted from each returned video. First, we adopt a min-Hashing based visual similarity in combination of the tagging similarity to group the returned videos. Based on the initial grouping configurations, we mine the co-occurred discriminative sub-signatures, based on which we iteratively refine the first step. Such iteration well handles the noise in visual content and tagging, since neither of which is fully trusted during the grouping. We validate our schemes on over 2,000 video clips crawled from a set of YouTube keyword query results. Comparing to alternative approaches, our scheme has shown superior robustness and precision.