Potential Semantics in Multi-modal Relevance Feedback Information for Image Retrieval

In image retrieval systems with interfaces of user relevance feedback, different users label different instances based on different image search results they prefer. In our previous work, we proposed a multi-model relevance feedback scheme for social image retrieval, which allows users to label relevance feedback information on different media modalities. These relevance feedback instances contain various potential semantics information related to users´ image targets. In this work-in-progress paper, we analyze various cases of user multi-model relevance feedback selections and their potential semantics to the targets, and then propose an idea of categorization for them. In future work, we will improve our approach to meets users´ requirements leveraging these knowledge.