Image scale determination for optimal texture classification using coordinated clusters representation.

Full Abstract

The efficiency of texture image classification is certainly influenced by image scale when a feature space or a classification method is not scale invariant. An alternative approach to the scale-invariant techniques is proposed that first estimates an effective image scale and then uses it to adjust texture features to get the best possible texture image recognition and classification. We use the correlation distance between pixels as a measure of the scale of texture images. We study the performance of classification of texture images in the coordinated cluster representation (CCR) versus an image scale and the size of the scanning window used for the coordinated cluster transform. Given the number of classes to be classified in, we find that an optimal (up to 100%) classification efficiency in the CCR feature space is obtained by changing an image scale and/or the size of the scanning window in the coordinated cluster transform.

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Author information

Author/s: Kurmyshev, Evguenii V (EV); Poterasu, Marian (M); Guillen-Bonilla, Jose T (JT);

Affiliation: Centro de Investigaciones en Optica, A.C., Loma del Bosque 115, Lomas del Campestre, 37150 León, Guanajuato, Mexico. kev(-atsign-)cio.mx

Journal and publication information

Publication Type: Journal Article

Journal: Applied optics (Appl Opt), published in United States. (Language: eng)

Reference: 2007-Mar; vol 46 (issue 9) : pp 1467-76

Dates: Created 2007/03/05; Completed 2007/08/01;

PMID: 17334437, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Pattern Recognition, Automated - methods

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