Learning from negative example in relevance feedback for content-based image retrieval

In this paper, we address some issues related to the combination of positive and negative examples to perform more efficient image retrieval. We analyze the relevance of negative example and how it can be interpreted. Then we propose a new relevance feedback model that integrates both positive and negative examples. First, a query is formulated using positive example, then negative example is used to refine the system's response. Mathematically, relevance feedback is formulated as an optimization of intra and inter variances of positive and negative examples.