Histogram clustering for unsupervised segmentation and image retrieval
Pattern Recognition Letters
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for Image Processing and Computer Vision
Algorithms for Image Processing and Computer Vision
Preface: Introduction to the special issue on evolutionary computer vision and image understanding
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
A new evolutionary algorithm for image segmentation
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Image segmentation using evolutionary computation
IEEE Transactions on Evolutionary Computation
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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 EvoSegalgorithm, which uses knowledge derived from texture analysis to identify how many homogeneous regions exist in the scene without a prioriinformation. EvoSeguses 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 EvoSegon synthetic and real world images and compare it with the state-of-the-art JSEG algorithm.