Steerable pyramid-based features for image retrieval from a texture database

Patrice Blancho; Hubert Konik; Kenneth Knoblauch

doi:10.1117/12.320146

17 July 1998 Steerable pyramid-based features for image retrieval from a texture database

Patrice Blancho, Hubert Konik, Kenneth Knoblauch

Proceedings Volume 3299, Human Vision and Electronic Imaging III; (1998) https://doi.org/10.1117/12.320146
Event: Photonics West '98 Electronic Imaging, 1998, San Jose, CA, United States

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.

Citation Download Citation

Patrice Blancho, Hubert Konik, and Kenneth Knoblauch "Steerable pyramid-based features for image retrieval from a texture database", Proc. SPIE 3299, Human Vision and Electronic Imaging III, (17 July 1998); https://doi.org/10.1117/12.320146

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