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Proceedings Paper

Steerable pyramid-based features for image retrieval from a texture database
Author(s): Patrice Blancho; Hubert Konik; Kenneth Knoblauch
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Paper Abstract

The measurement of perceptual similarities between textures is a difficult problem in applications such as image classification and image retrieval in large databases. Among the various texture analysis methods or models developed over the years, those based on a multi-scale multi- orientation paradigm seem to give more reliable results with respect to human visual judgement. This paper describes new texture features extracted from an overcomplete wavelet transform called a `steerable pyramid' which models human early vision. The textured image is decomposed into a 3- level pyramid using a 4-orientation band filter set, the texture features are computed from the distributions associated with each filter as follows: we construct the `cumulative distribution function' (cdf) of graylevels from the 12 band-pass images and we fit them with Bezier curves in order to characterize the texture. The clusters of the Bezier control points from the 12 cdf allow us to discriminate the textures. We apply these new texture features to the search through an image database to find the most `similar' textures to a selected one.

Paper Details

Date Published: 17 July 1998
PDF: 11 pages
Proc. SPIE 3299, Human Vision and Electronic Imaging III, (17 July 1998); doi: 10.1117/12.320146
Show Author Affiliations
Patrice Blancho, Univ. Jean Monnet (France)
Hubert Konik, Univ. Jean Monnet (France)
Kenneth Knoblauch, Univ. Jean Monnet (France)

Published in SPIE Proceedings Vol. 3299:
Human Vision and Electronic Imaging III
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

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