The study on the feedback of large scale content-based video retrieval
Qi Xiangdong, Dawei Liu, Jinlin Wang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2013
This paper addresses the problem of large scale content-based video retrieval with relevance feedback. We analyze the common methods which leverage local feature detectors to extract feature descriptors from video collections and perform multi-level matching after indexing and retrieval of feature vectors. Instead of learning similarity-preserving codes, an approach of relevance feedback in a light-weight way is proposed. A relevance model is proposed to merge semantic similarity with the original distance matching at descriptor level. By learning several weights using canonical correlation analysis (CCA), the resulting candidate list of similar videos changes according to relevance feedback. Finally, we demonstrate the improvement of the proposed method by experiments on a standard real world dataset.