Image semantics discovery from web pages for semantic-based image retrieval using self-organizing maps

Traditional content-based image retrieval (CBIR) systems often fail to meet a user's need due to the `semantic gap' between the extracted features of the systems and the user's query. The cause of the semantic gap is the failure of extracting real semantics from an image and the query. To extract semantics of images, however, is a difficult task. Most existing techniques apply some predefined semantic categories and assign the images to appropriate categories through some learning processes. Nevertheless, these techniques always need human intervention and rely on content-based features. In this paper we propose a novel approach to bridge the semantic gap which is the major deficiency of CBIR systems. We conquer the deficiency by extracting semantics of an image from the environmental texts around it. Since an image generally co-exists with accompanying texts in various formats, we may rely on such environmental texts to discover the semantics of the image. We apply a t...

• Type: Journal
• Year: 2008
• Where: ESWA
• Authors: Hsin-Chang Yang, Chung-Hong Lee
• Keywords: ESWA 2008, Images, Semantic, Semantic Gap