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Proceedings Paper

Semantics-based image retrieval by text mining on environmental texts
Author(s): Hsin-Chang Yang; Chung-Hong Lee
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Paper Abstract

In this paper we propose a novel method to bridge the 'semantic gap' between a user's information need and the image content. The semantic gap describes the major deficiency of content-based image retrieval (CBIR) systems which use visual features extracted from images to describe the images. We conquer the deficiency by extracting semantic of an image from the environmental texts around it. Since an image generally co-exists with accompany texts in various formats, we may rely on such environmental texts to discover the semantic of the image. A text mining approach based on self-organizing maps is used to extract the semantic of an image from its environmental texts. We performed experiments on a small set of images and obtained promising results.

Paper Details

Date Published: 13 January 2003
PDF: 12 pages
Proc. SPIE 5010, Document Recognition and Retrieval X, (13 January 2003); doi: 10.1117/12.476039
Show Author Affiliations
Hsin-Chang Yang, Chang Jung Univ. (Taiwan)
Chung-Hong Lee, National Kaohsiung Univ. of Applied Sciences (Taiwan)

Published in SPIE Proceedings Vol. 5010:
Document Recognition and Retrieval X
Tapas Kanungo; Elisa H. Barney Smith; Jianying Hu; Paul B. Kantor, Editor(s)

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