Automatic color image segmentation

Color Image Segmentation partition the image into distinct regions of similar pixels based on pixel property. It is the high level image description in terms of objects, scenes and features. The success of image analysis depends on segmentation reliability. The accurate partition of the image into regions is a challenging task. K-Means Clustering algorithm is the popular unsupervised clustering for dividing the images into multiple regions based on image color property. The major issue of the algorithm is that the user has to specify the number of clusters-K, which is used to split the image into K regions. To overcome the issue, this paper is focused on determining K automatically based on local maxima of gray level co-occurrence matrix. Automatic generated K value is then passed to Fast K-means Clustering algorithm for segmenting color images into multiple regions. Proposed approach achieved better results than earlier K-Means and gives feasible solution for color image segmentation which may be helpful in semantic based image retrieval.