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Proceedings Paper

Image retrieval and reversible illumination normalization
Author(s): Longin Jan Latecki; Venugopal Rajagopal; Ari Gross
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Paper Abstract

We propose a novel approach to retrieve similar images from image databases that works in the presence of significant illumination variations. The most common method to compensate for illumination changes is to perform color normalization. The existing approaches to color normalization tend to destroy image content in that they map distinct color values to identical color values in the transformed color space. From the mathematical point of view, the normalization transformation is not reversible. In this paper we propose to use a reversible illumination normalization transformation. Thus, we are able to compensate for illumination changes without any reduction of content information. Since natural illumination changes affect different parts of images in different amounts, we apply our transformation locally to sub-images. Basic idea is to divide an image into sub-images, normalize each one separately, and then project it to an n-dimensional reduced space using principal component analysis. This process yields a normalized texture representation as a set of n-vectors. Finding similar images is now reduced to computing distances between sets of n-vectors. Results were compared with a leading image retrieval system.

Paper Details

Date Published: 17 January 2005
PDF: 12 pages
Proc. SPIE 5670, Internet Imaging VI, (17 January 2005);
Show Author Affiliations
Longin Jan Latecki, Temple Univ. (United States)
Venugopal Rajagopal, Temple Univ. (United States)
Ari Gross, CUNY/Queens College (United States)

Published in SPIE Proceedings Vol. 5670:
Internet Imaging VI
Simone Santini; Raimondo Schettini; Theo Gevers, Editor(s)

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