Bridging the semantic gap in content-based image retrieval systems

摘要

Content-based image retrieval (CBIR) is receiving much attention because of the ever growing amount of pictorial content in digital libraries world wide. Much of this content is insufficiently supplied with textual metadata because it is beyond either financial or time bars to generate those data. CBIR systems operate only on the images by extraction of visual primitives like color, texture or shape. However, there is a downside to this approach: It is not possible to extract full semantic information from images alone, a problem known as the semantic gap. This paper introduces an approach that aims at bridging this gap and thereby improving the performance of the system. This is achieved by making use of user feedback to cluster images into different thematic groups. The feedback is used for the global improvement of the system instead of just in the scope of one query. This leads to a system that is continuously learning the semantics of the image base. A prototype has been implemented and an evaluation measuring it against a commercial system has been done. The results show a significant increase both in recall and in precision.