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Proceedings Paper

Fast texture database retrieval using extended fractal features
Author(s): Lance M. Kaplan; Romain Murenzi; Kameswara Rao Namuduri
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Paper Abstract

The increase in the number of multimedia databases consisting of images has created a need for a quick method to search these databases for a particular type of image. An image retrieval system will output images from the database similar to the query image in terms of shape, color, and texture. For the scope of our work, we study the performance of multiscale Hurst parameters as texture features for database image retrieval over a database consisting of homogeneous textures. These extended Hurst features represent a generalization of the Hurst parameter for fractional Brownian motion (fBm) where the extended parameters quantize the texture roughness of an image at various scales. We compare the retrieval performance of the extended parameters against traditional Hurst features and features obtained from the Gabor wavelet. Gabor wavelets have previously been suggested for image retrieval applications because they can be tuned to obtain texture information for a number of different scales and orientations. In our experiments, we form a database combining textures from the Bonn, Brodatz, and MIT VisTex databases. Over the hybrid database, the extended fractal features were able to retrieve a higher percentage of similar textures than the Gabor features. Furthermore, the fractal features are faster to compute than the Gabor features.

Paper Details

Date Published: 23 December 1997
PDF: 12 pages
Proc. SPIE 3312, Storage and Retrieval for Image and Video Databases VI, (23 December 1997); doi: 10.1117/12.298440
Show Author Affiliations
Lance M. Kaplan, Clark Atlanta Univ. (United States)
Romain Murenzi, Clark Atlanta Univ. (United States)
Kameswara Rao Namuduri, Clark Atlanta Univ. (United States)


Published in SPIE Proceedings Vol. 3312:
Storage and Retrieval for Image and Video Databases VI
Ishwar K. Sethi; Ramesh C. Jain, Editor(s)

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