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Image retrieval based on high level concept detection and semantic labelling

This paper presents a novel approach to high-level concept detection and retrieval in images based on a combination of visual thesaurus and multi-class supervised learning. The visual thesaurus includes both conceptual and spatial location information of semantic concepts that are key to image labelling. Our image annotation (or labelling) process includes segmenting and building an image signature. The visual thesaurus is then built using a multi-class supervised SYM classifier. Algorithm for spatial location matching is included. Similarity matching during retrieval is performed on both the content as well as the location information using the standard Euclidean distance. Corel data set was used for experimentation and results were compared with two related approaches to visual thesaurus and image retrieval.