Adaptive clustering of image database (ACID) as an efficient tool for improving retrieval in a CBIR system

The paper describes a content-based image retrieval (CBIR) system with relevance feedback (RF). Instead of standard relevance feedback procedure, an adaptive clustering of image database (ACID) according to particular subjective needs is introduced in our system. Images labeled by the user as relevant are collected in clusters, and their representative members are used in further searching procedure instead of all images contained in the database. By this way, some history of previous retrieving is embedded into a searching process enabling faster and more subjective retrieval. Moreover, clusters are adaptively updated after each retrieving session, following actual user's needs. The efficiency of the proposed ACID system is tested with images from Corel and MIT datasets.