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Proceedings Paper

Adaptive filtering and indexing for image databases
Author(s): Albert D. Alexandrov; Wei Y. Ma; Amr El Abbadi; B. S. Manjunath
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Paper Abstract

In this paper we combine image feature extraction with indexing techniques for efficient retrieval in large texture images databases. A 2D image signal is processed using a set of Gabor filters to derive a 120 component feature vector representing the image. The feature components are ordered based on the relative importance in characterizing a given texture pattern, and this facilitates the development of efficient indexing mechanisms. We propose three different sets of indexing features based on the best feature, the average feature and a combination of both. We investigate the tradeoff between accuracy and discriminating power using these different indexing approaches, and conclude that the combination of best feature and the average feature gives the best results.

Paper Details

Date Published: 23 March 1995
PDF: 12 pages
Proc. SPIE 2420, Storage and Retrieval for Image and Video Databases III, (23 March 1995); doi: 10.1117/12.205292
Show Author Affiliations
Albert D. Alexandrov, Univ. of California/Santa Barbara (United States)
Wei Y. Ma, Univ. of California/Santa Barbara (United States)
Amr El Abbadi, Univ. of California/Santa Barbara (United States)
B. S. Manjunath, Univ. of California/Santa Barbara (United States)


Published in SPIE Proceedings Vol. 2420:
Storage and Retrieval for Image and Video Databases III
Wayne Niblack; Ramesh C. Jain, Editor(s)

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