Faceted Subtopic Retrieval: Exploiting the Topic Hierarchy via a Multi-modal Framework

Abstract

The overwhelming amount of web videos posted on the social media websites make effective browsing and search a challenging task. The user-provided metadata, has been proved useful in large-scale video organization and retrieval. Search result clustering, which utilizes the associated metadata to cluster the returned results into semantic groups according to its involved subtopics, has shown its advantages. Most of the existing works on search result clustering are devoted to solving the ambiguous problem resulted from general queries. In this paper, we propose the problem of faceted subtopic retrieval, which focus on more complex queries concerning political and social events or issues. Hierarchical topic model (hLDA) is adapted to exploit the intrinsic topic hierarchy inside the retrieved collections. Furthermore, this paper offers a new perspective of multi-modal video analysis by exploring the pairwise visual cues deriving from duplicate detection for constrained topic modeling. We modify the standard hierarchical topic model by integrating: 1) query related Supervision knowledge (ShLDA) and 2) duplicate Relation constraints (RShLDA). Carefully designed experiments on web-scale video dataset validate the proposed method.