A fuzzy clustering algorithm with spatial robust estimation constraint for noisy color image segmentation

2013 ◽  
Vol 34 (4) ◽  
pp. 400-413 ◽  
Author(s):  
Dante Mújica-Vargas ◽  
Francisco J. Gallegos-Funes ◽  
Alberto J. Rosales-Silva
Keyword(s):  
Image Segmentation ◽  
Robust Estimation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Color Image ◽  
Color Image Segmentation ◽  
Fuzzy Clustering Algorithm

Semantic Color Retrieval of Color Image Based on Fuzzy Clustering

2013 ◽  
Vol 380-384 ◽  
pp. 3469-3473
Author(s):  
Xiao Feng Wang ◽  
Jian Hua Li
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Color Image ◽  
Color Image Segmentation ◽  
Fuzzy Clustering Method ◽  
Retrieval Mechanism ◽  
Previous Algorithm ◽  
Physical Features ◽  
Initial Focus

As next generation of the web, the semantic web aims at a more intelligent web severing machines as well as people, based on radical notions of information sharing and acquisition. For color image segmentation, semantic color is our focus. One method of color partition is fuzzy clustering which has been widely used in image segmentation. However, the fuzzy clustering algorithm is parameter sensitive, and lack of availability because of its initial focus on physical features. To improve the above problems, a novel fuzzy clustering method based on semantic color retrieval for image segmentation is proposed in this paper. The method is realized by modifying the membership function in the conventional clustering algorithm and by constructing the semantic color retrieval mechanism to achieve the semantic color extraction, which take human visual subjectivity into account in semantics. Experimental results show that the presented method performs more effectively than the previous algorithm.

AN EXTENDED FUZZY CLUSTERING ALGORITHM AND ITS APPLICATION

1995 ◽  
Vol 05 (02) ◽  
pp. 239-259
Author(s):  
SU HWAN KIM ◽  
SEON WOOK KIM ◽  
TAE WON RHEE
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Main Memory ◽  
Color Image Segmentation ◽  
Occurrence Rate ◽  
Secondary Memory ◽  
Worst Case ◽  
Memory Space ◽  
Fuzzy Clustering Algorithm

For data analyses, it is very important to combine data with similar attribute values into a categorically homogeneous subset, called a cluster, and this technique is called clustering. Generally crisp clustering algorithms are weak in noise, because each datum should be assigned to exactly one cluster. In order to solve the problem, a fuzzy c-means, a fuzzy maximum likelihood estimation, and an optimal fuzzy clustering algorithms in the fuzzy set theory have been proposed. They, however, require a lot of processing time because of exhaustive iteration with an amount of data and their memberships. Especially large memory space results in the degradation of performance in real-time processing applications, because it takes too much time to swap between the main memory and the secondary memory. To overcome these limitations, an extended fuzzy clustering algorithm based on an unsupervised optimal fuzzy clustering algorithm is proposed in this paper. This algorithm assigns a weight factor to each distinct datum considering its occurrence rate. Also, the proposed extended fuzzy clustering algorithm considers the degree of importances of each attribute, which determines the characteristics of the data. The worst case is that the whole data has an uniformly normal distribution, which means the importance of all attributes are the same. The proposed extended fuzzy clustering algorithm has better performance than the unsupervised optimal fuzzy clustering algorithm in terms of memory space and execution time in most cases. For simulation the proposed algorithm is applied to color image segmentation. Also automatic target detection and multipeak detection are considered as applications. These schemes can be applied to any other fuzzy clustering algorithms.

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