Feature Re-weighting in Content-Based Image Retrieval

Relevance Feedback (RF) is a useful technique in reducing semantic gap which is a bottleneck in Content-Based Image Retrieval (CBIR). One of the classical approaches to implement RF is feature re-weighting where weights in the similarity measure are modified using feedback samples as returned by the user. The main issues in RF are learning the system parameters from feedback samples and the high-dimensional feature space. We addressed the second problem in our previous work, here, we focus on the first problem. In this paper, we investigated different weight update schemes and compared the retrieval results. We proposed a new feature re-weighting method which we tested on three different image databases of size varying between 2000 and 8365, and having number of categories between 10 and 98. The experimental results with scope values of 20 and 100 demonstrated the superiority of our method in terms of retrieval accuracy.