Using Long-Term Learning to Improve Efficiency of Content-Based Image Retrieval

Abstract. Content-based image retrieval (CBIR) is an emerging research field, studying retrieval of images from unannotated databases. In CBIR, images are indexed on the basis of low-level statistical features that can be automatically derived from the images. Due to the gap between high-level semantic concepts and low-level visual features, the performance of CBIR applications often remains quite modest. One method for improving CBIR results is to try to learn the user’s preferences with intra-query learning methods such as relevance feedback. However, relevance feedback provides user interaction information which can automatically be used also in long-term or inter-query learning. In this paper, a method for using long-term learning in our PicSOM system is presented. The performed experiments show that the system readily supports the presented user interaction feature and that the efficiency of the system can be substantially increased by using it in parallel with the MPEG-7 visual descriptors