Concept learning and transplantation for dynamic image databases

The task of a content-based image retrieval (CBIR) system is to cater to users who expect to get relevant images with high precision and efficiency in response to query images. This paper presents a concept learning approach that integrates a mixture model of the data, relevance feedback and long-term continuous learning. The concepts are incrementally refined with increased retrieval experiences. The concept knowledge can be immediately transplanted to deal with the dynamic database situations such as insertion of new images, removal of existing images and query images, which are outside the database. Experimental results on Corel database show the efficacy of our approach.