Dominant peak-means clustering for color image segmentation
Tapaswini Pattnaik, Priyadarshi Kanungo · 2017
This paper presents a fully automatic color image segmentation. Colors of the pixels are used to partition the color space into clusters. Firstly the dominant peaks of the histograms of Red, Green, and Blue planes are detected using a peak detection algorithm. Then dominant peaks of these three histograms are used to define the initial number of clusters and their centroids. Finally a clustering algorithm is used to update the number of clusters and their centroids. Using these final cluster centroids each pixel is labeled to one of these clusters to segment the color image. The proposed algorithm is validated using Berkeley and Corel image data set. The result shows that our proposed method is faster and better in comparison to one of the land mark method JSEG.