UNSPECIFIED (2003) Unsupervised texture segmentation using multiresolution hybrid genetic algorithm. In: IEEE International Conference on Image Processing, SEP 14-17, 2003, BARCELONA, SPAIN.

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Abstract

This work approaches the texture segmentation problem by incorporating genetic algorithm and k-mean clustering method within a multiresolution structure. First, a quad-tree structure is constructed and the input image is partition into blocks at different resolution levels. Texture features are then extracted from each block. Based on the texture features, a hybrid genetic algorithm is employed to perform the segmentation. The crossover operator of traditional genetic algorithm is replaced with k-means clustering method while the mutate and select operators are adopted. In the final step, the boundaries and the segmentation result of the current resolution level are propagated down to the next level to act as contextual constraints and the initial configuration of the next level, respectively.

Item Type:	Conference Item (UNSPECIFIED)
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TR Photography
Journal or Publication Title:	2003 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL 2, PROCEEDINGS
Publisher:	IEEE
ISBN:	0-7803-7750-8
Date:	2003
Number of Pages:	4
Page Range:	pp. 1033-1036
Publication Status:	Published
Title of Event:	IEEE International Conference on Image Processing
Location of Event:	BARCELONA, SPAIN
Date(s) of Event:	SEP 14-17, 2003
URI:	http://wrap.warwick.ac.uk/id/eprint/9035

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