Simple comparison of image segmentation algorithms based on evaluation criterion

In the image analysis, image segmentation is the operation that divides image into set of different segments. The paper deals about common color image segmentation techniques and methods. The advantages and disadvantage of each one will be described in this paper. At the end of the paper, the evaluation criterion will be introduced and applied on the algorithms results. Five most used image segmentation algorithms, namely, Efficient graph based, K-means, Mean shift, Expectation maximization and hybrid method are compared by designed criterion.

[1]  Yong Zhu,et al.  A Hybrid Image Segmentation Approach Based on Mean Shift and Fuzzy C-Means , 2009, 2009 Asia-Pacific Conference on Information Processing.

[2]  Liu Wei,et al.  Image Segmentation Based on the Mean-Shift in the HSV Space , 2006, 2007 Chinese Control Conference.

[3]  Ge Yu,et al.  A Fast Algorithm for Color Image Segmentation , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[4]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[5]  R.A. Salam,et al.  Hybrid of Mean-shift and median-cut algorithm for fish segmentation , 2008, 2008 International Conference on Electronic Design.

[6]  Tomer Hertz,et al.  Computing Gaussian Mixture Models with EM Using Equivalence Constraints , 2003, NIPS.

[7]  Yu-Jin Zhang,et al.  Advances in image and video segmentation , 2006 .

[8]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Zhang Xue-xi,et al.  Hybrid intelligent algorithms for color image segmentation , 2008, 2008 Chinese Control and Decision Conference.