In this paper, we present a novel idea of co-clustering image features and semantic concepts. We accomplish this by modelling user feedback logs and low-level features using a bipartite graph. Our experiments demonstrate that (1) incorporating semantic information achieves better image clustering and (2) feature selection in co-clustering narrows the semantic gap, thus enabling efficient image retrieval.
Date of creation, presentation, or exhibit
Department, Program, or Center
Computer Science (GCCIS)
Co-Clustering Image Features and Semantic Concepts, Proceedings of IEEE ICIP 2006. Held in Atlanta, GA: 8-11 October 2006.
RIT – Main Campus