Traditional content-based image retrieval (CBIR) systems often fail to fulfill a user's need due to the 'semantic gap' existed between the extracted features of the systems and the user's query. 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. We apply a text mining process, which adopts the self-organizing map (SOM) learning algorithm as a kernel, on the environmental texts of an image to extract the semantic information from this image. Some implicit semantic information of the images can be discovered after the text mining process. We also define a semantic relevance measure to achieve the semantic-based image retrieval task. We performed experiments on a set of images which are collected from web pages and obtained promising results.
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