Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of kernel and spectral methods for clustering
Pattern Recognition
An improved algorithm for clustering gene expression data
Bioinformatics
Theoretical advances in artificial immune systems
Theoretical Computer Science
Iterative weighted maximum likelihood denoising with probabilistic patch-based weights
IEEE Transactions on Image Processing
A robust fuzzy local information C-means clustering algorithm
IEEE Transactions on Image Processing
Some connectivity based cluster validity indices
Applied Soft Computing
Nonparametric genetic clustering: comparison of validity indices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Image segmentation using a texture gradient based watershed transform
IEEE Transactions on Image Processing
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
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Artificial immune system (AIS) has been proven effective for pattern classification by its characteristics of learning and adaptability from the vertebrate immune system. However, little focus has been placed on the synthetic aperture radar (SAR) image segmentation by AIS. In this paper, we present an efficient automatic framework in AIS for SAR image segmentation. It aims at simultaneously solving the following three different crucial issues: (1) the automatic ability of searching true number of land-cover in SAR images; (2) the objective functions in guiding the segmentation of the images with complicated multiplicative noises; and (3) reduction of the computational complexity of segmenting SAR images with large sizes. By the proposed framework here, it can mitigate the above difficulties to a certain degree. Furthermore, a reasonable spatial filtering and watershed transformation are employed in the initial stage of the framework, and then a novel clustering index in Gaussian kernel is designed to lead the searching process, which is beneficial to find the partitions for the clustering problem with highly overlapping and contaminating samples. Besides, we also propose an efficient computing paradigm in AIS with variable length of chromosomes to search the optimal partitions of SAR images, which can find the optimal numbers of clusters automatically. Finally, in order to speed up the segmentation process of SAR image, we employ the histogram statistics to implement the pixels partition; therefore, the segmenting time is dependent on the small number of gray-levels, not the great amounts of whole image pixels. To test the segmentation performances of the proposed algorithm, a detailed experimental analysis was conducted on two simulated SAR images and four complicated real ones. Other four state-of-the-art image segmentation methods are employed for comparison, which are genetic clustering by variable string length encoding (VGA), fast generalized fuzzy C-means clustering (FGFCM), fuzzy local information C-means clustering algorithm (FLICM) and graph partitioning method: spectral clustering ensemble (SCE).