City Research Online

Abstract

A feature selection methodology based on a novel Bhattacharyya space is presented and illustrated with a texture segmentation problem. The Bhattacharyya space is constructed from the Bhattacharyya distances of different measurements extracted with sub-band filters from training samples. The marginal distributions of the Bhattacharyya space present a sequence of the most discriminant sub-bands that can be used as a path for a wrapper algorithm. When this feature selection is used with a multiresolution classification algorithm on a standard set of texture mosaics, it produces the lowest misclassification errors reported.

Publication Type:	Article
Additional Information:	NOTICE: this is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition, Volume 39, Issue 5, May 2006, Pages 812–826, http://dx.doi.org/10.1016/j.patcog.2005.12.003
Publisher Keywords:	Feature selection, Bhattacharyya distance/space, Texture segmentation
Subjects:	Q Science > QA Mathematics
Departments:	School of Science & Technology > Engineering School of Science & Technology > Computer Science > giCentre
SWORD Depositor:	Symplectic Administrator

Export

Downloads