Capturing semantic relationship among images in clusters for efficient content-based image retrieval

This paper presents an efficient content-based image retrieval system that captures users' semantic concepts in clusters. These semantically homogeneous clusters aid in the retrieval system to accurately measure the semantic similarity among images and therefore reduce the semantic gap. They also aid in the retrieval system to find matched images in a few candidate clusters and therefore reduce the search space. The extensive experiments demonstrate that the proposed retrieval system outperforms the peer systems to quickly retrieve the desired images in a few iterations.