Relevance feedback for semantics based image retrieval

Content based image retrieval is one of the most active research areas in the field of multimedia technology. Currently, the relevance feedback approach has attracted great attention since it can bridge the gap between low-level features and the semantics of images. We propose a new relevance feedback technique, which uses the normal mixture model for the high-level similarity metric of the user´s intention and estimates the unknown parameters from the user´s feedback. Our approach is based on a novel hybrid algorithm where the criterion for the selection of the display image set is evolved from the most informative to the most probable as the retrieval process progresses. Experiments on the Corel image set show that the proposed algorithm outperforms MindReader at the semantics based search