Title: Color Image Segmentation Features and Techniques: A Comparative Study

Year of Publication: Sep - 2016
Page Numbers: 1-7
Authors: Jalal Omer Atoum, Aalaa Albadarne
Conference Name: The International Conference on Data Mining, Multimedia, Image Processing and their Applications (ICDMMIPA2016)
- Malaysia


Image segmentation is an important and interesting digital image pre-processing phase to enhance the performance of various pattern recognition and computer vision applications. Segmentation process enhance images analysis through the extractions of features from the relevance part of image only. In this paper, a comparative study between five different color segmentation techniques is performed. The experimental results of PSNR and MSE metrics show that K-means clustering algorithm has better results when compared to the other algorithms, but still need to be modified to deal with different types of sharp and smooth edges.