An algorithm for color image segmentation, based on color and spatial information is proposed in this paper. First, color quantization is performed on an image based on the proposed color coarseness metric, and then an incremental region growing method is exploited to find the spatial connectivity of pixels with similar colors to form the initial segmented regions. Second, the initial regions are hierarchically merged based on the region distance defined by the color and spatial information. A criteria is proposed to decide the termination of the merging process. Finally, the erosion and dilation operators are used to smooth the edges of the segmented regions. The experimental results demonstrate that the color image segmentation results of the proposed approach hold favorable consistency in terms of human perception.
[1]Zhang YJ. Image Engineering. Beijing: Tsinghua University Press, 1999. 179~180 (in Chinese).
[2]Lucchese L, Mitra SK. Unsupervised segmentation of color mages based on K-means clustering in the chromaticity plane. In: Collins F, ed. Proc. of the Content-Based Access of Image and Video Libraries. Los Alamitos, CA: IEEE Computer Society Press, 1999. 74~78. http://csdl.computer.org/comp/proceedings/cbaivl/ 1999/0034/00/00340074abs.htm
[3]Tuan DP. Image segmentation using probabilistic fuzzy C-means clustering. In: Mercer B, ed. Proc. of the Int'l Conf. on Image Processing. IEEE Signal Processing Society Press, 2001. 722~725. http://ieeexplore.ieee.org/xpl/conferences.jsp
[4]Malik J, Belongie F, Leugn T, Shi JB. Contour and texture analysis for image segmentation. Int'l Journal of Computer Vision, 2001,43(1):7~27.
[5]Deng YN, Manjunath BS. Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2001,23(8):800~810.
[6]Haris K, Efstratiadis S. Hybrid image segmentation using watersheds and fast region merging. IEEE Trans. on Image Processing, 1998,7(12):1684~1699.
[7]Zhu SC, Yuille A. Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1996,18(9):884~900.
[8]Fan JP, Yau DKY, Elmagarmid AK, Aref WG. Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Trans. on Image Processing, 2001,10(10):1454~1466.
[9]Sonka M, Vaclav H, Boyle R. Image Processing, Analysis, and Machine Vision. Beijing: People's Posts & Telecommunications Publishing House, 2002. 123~216.
[10]Deng YN, Kenney C, Moore MS, Manjunath BS. Peer group filtering and perceptual color image quantization. In: Cortadella J, ed. Proc. of the IEEE Int'l Symp. on Circuits and Systems. Los Alamitos, CA: IEEE Computer Society Press, 1999. 21~24.
[11]Arya S, Mount D. Algorithms for fast vector quantization. In: Proc. of the Data Compression Conf. Los Alamitos, CA: IEEE Computer Society Press , 1993. 381~390. http://www.cs. umd.edu/~mount/Papers/DCC.pdf
[12]Ruan QQ. Digital Image Processing. Beijing: Publishing House of Electronics Industry, 2001. 450~451 (in Chinese).
[13]Martin D, Fowlkes C, Tal D, Malik J. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Werner B, ed. Proc. of the Int'l Conf. on Computer Vision. Los Alamitos, CA: IEEE Computer Society Press, 2001. 416~423. http:// www.cs.bc.edu/~dmartin/papers/pocv01.pdf
[1]章毓晋.图像工程.北京:清华大学出版社,1999.179~180.