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EVOW
2007
Springer

Unsupervised Evolutionary Segmentation Algorithm Based on Texture Analysis

9 years 1 months ago
Unsupervised Evolutionary Segmentation Algorithm Based on Texture Analysis
Abstract. This work describes an evolutionary approach to texture segmentation, a long-standing and important problem in computer vision. The difficulty of the problem can be related to the fact that real world textures are complex to model and analyze. In this way, segmenting texture images is hard to achieve due to irregular regions found in textures. We present our EvoSeg algorithm, which uses knowledge derived from texture analysis to identify how many homogeneous regions exist in the scene without a priori information. EvoSeg uses texture features derived from the Gray Level Cooccurrence Matrix and optimizes a fitness measure, based on the minimum variance criteria, using a hierarchical GA. We present qualitative results by applying EvoSeg on synthetic and real world images and compare it with the state-of-the-art JSEG algorithm.
Cynthia B. Pérez, Gustavo Olague
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where EVOW
Authors Cynthia B. Pérez, Gustavo Olague
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