Color image segmentation using adaptive unsupervised clustering approach

This paper presents the Region Splitting and Merging-Fuzzy C-means Hybrid Algorithm (RFHA), an adaptive unsupervised clustering approach for color image segmentation, which is important in image analysis and in understanding pattern recognition and computer vision field. Histogram thresholding technique is applied in the formation of all possible cells, used to split the image into multiple homogeneous regions. The merging technique is applied to merge perceptually close homogeneous regions and obtain better initialization for the Fuzzy C-means clustering approach. Experimental results have demonstrated that the proposed scheme could obtain promising segmentation results, with 12% average improvement in clustering quality and 63% reduction in classification error compared with other existing segmentation approaches.

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► This paper proposes an adaptive unsupervised clustering approach, abbreviated as RFHA.
► RSM is proposed to determine the initial cluster number and cluster center.
► RFHA has successfully been tested on the public segmentation database images.
► RFHA outperforms other state-of-the-art clustering techniques.
► The segmentation results produced are more homogenous and less classification error.