A Cooperative Learning Scheme for Interactive Video Search

Abstract

The main idea of an interactive search is to gradually improve search quality of retrieval system via user interaction. While a large amount of work has been made in the past, most of the existing approaches typically require labeling effort for updating the query model. Unfortunately, it is time-consuming and tedious to label a large number of training examples. We aim to develop a novel text-driven cooperative learning scheme, which can offer users a quite natural query fashion and alleviate significantly the burden on users without compromising search performance. Starting with an advanced text-driven video search engine, a multi-view cooperative training strategy is proposed for learning from feedback data a refined ranking function. The main merit of proposed framework is its ability in mining training samples automatically from previous answer set and implicitly combining multiple modalities for effectively learning users' query intent. Evaluation on TRECVID' 06 video corpus shows that the proposed scheme with few training seeds achieves a comparable performance with classic interactive schemes.