Relevance graph-based image retrieval

The basic limitation of content-based image retrieval and relevance feedback based on low-level image features is that low-level features are often highly ineffective for representing nor only content similarity, but conceptual and contextual similarity between images. On the other hand, the utility of text-based image retrieval is restricted due to the limited availability of image annotations and textual description's limited ability in describing image content. In this paper, we introduce a novel approach to content-, concept- and context-based image retrieval that utilizes user-established relevance between images only using image links without relying on image features or textual annotations. We present a framework for accumulating image relevance information through relevance feedback, determining the degree of relevance, and constructing a relevance graph for an image database. The use of graph-theoretical algorithms is suggested for image search and experimental studies are presented to demonstrate the potential of the proposed methods.