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Proceedings Paper

Texture features for image classification and retrieval
Author(s): Mohamed Borchani; Georges Stamon
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Paper Abstract

In this paper, we present an approach to texture-based image retrieval using image similarity on the basis of the matching of selected texture features. Image texture features are generated via gray level co-occurrence matrix, run-length matrix, and image histogram. Since they are computed over gray levels, color images of the database are first converted to 256 gray levels. For each image of the database, a set of texture features is extracted. They are derived from a modified form of the gray level co-occurrence matrix over several angles and distances, from a modified form of the run-length matrix over several angles, and from the image histogram. A sequential forward search is performed on all these features to reduce the dimensionality of the feature space. A supervised classifier is then applied to this reduced feature space in order to classify images into well separated classes. For measuring the similarity between two images a distance between two texture feature vectors is calculated. First experiments with multiple queries in a large image database give good results in terms of both speed and classification rate.

Paper Details

Date Published: 6 October 1997
PDF: 6 pages
Proc. SPIE 3229, Multimedia Storage and Archiving Systems II, (6 October 1997); doi: 10.1117/12.290360
Show Author Affiliations
Mohamed Borchani, Univ. de Paris V--Univ. Rene Descartes (France)
Georges Stamon, Univ. de Paris V--Univ. Rene Descartes (France)

Published in SPIE Proceedings Vol. 3229:
Multimedia Storage and Archiving Systems II
C.-C. Jay Kuo; Shih-Fu Chang; Venkat N. Gudivada, Editor(s)

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