Hierarchical feedback algorithm based on visual community discovery for interactive video retrieval

Community structure as an interesting property of networks has attracted wide attention from many research fields. In this paper, we exploit the visual community structure in visual-temporal correlation network and use it to facilitate interactive video retrieval. We propose a hierarchical community-based feedback algorithm (HieCommunityRank) to make full use of the limited user feedback by integrating the most informative context according to visual community semantics. Since it re-ranks video shots respectively through diffusion process in inter-community and intra-community level, HieCommunityRank can guarantee both the global diverse distribution and the local consistency of video shots. Meanwhile it can get fast responsiveness after user feedback, which is rather important facing large amount of video collections. Experiments on TRECVID 09 Search dataset demonstrate the effectiveness and efficiency of the proposed algorithm.