Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Improving fuzzy c-means clustering based on feature-weight learning
Pattern Recognition Letters
Robust kernel FCM in segmentation of breast medical images
Expert Systems with Applications: An International Journal
A new approach to image retrieval with hierarchical color clustering
IEEE Transactions on Circuits and Systems for Video Technology
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Image segmentation as a big part of computer vision has been the hot research topic nowadays. Fuzzy c-means FCM algorithm, as the standard and basic approach, is widely adopted by most researchers. In this paper, authors deeply analyse some defects of this technique, such as presetting clustering centre, initialling cluster centres randomly and heavy computation load. To overcome these drawbacks, a novel approach is proposed based on the combination of hierarchal subtractive clustering and fuzzy c-means, in which hierarchal subtractive clustering is introduced for simplifying the cluster centres fixing process and speeding up convergence. A series steps has been offered to imply how to implement this approach, and finally, experiments on two colour images show that, without damaging clustering validity and effectiveness, the novel approach is superior on clustering speed compared to FCM, it is the better choice for dealing with today's high resolution colour images.