Texture descriptors based on co-occurrence matrices
Computer Vision, Graphics, and Image Processing
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning using an artificial immune system
Journal of Network and Computer Applications - Special issue on intelligent systems: design and applications. Part 2
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
RABNET: a real-valued antibody network for data clustering
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Frame representations for texture segmentation
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
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This paper presents a novel texture segmentation scheme based on two techniques: watershed and a novel structural adaptation artificial immune antibody competitive network (SAIANet). The proposed scheme first partitions image into a set of regions by watershed algorithm and then clusters the watershed regions by SAIANet, where the gray level co-occurrence matrix and the wavelet frame texture features are extracted from each watershed region as the antigens of SAIANet. A new immune antibody neighborhood and an adaptive learning coefficient are presented, and inspired by the long-term memory in cerebral cortices, a long-term memory coefficient is introduced into the network. The minimal spanning tree in graph theory is used to automatically cluster antibody obtained in the output space without a predefined number of clustering. Finally, the presented SAIANet is devoted to performing a fully unsupervised texture segmentation with a superior performance, which makes full use of the watershed segmentation results.