{"title":"A Comparative Study of Image Segmentation Algorithms","authors":"Mehdi Hosseinzadeh, Parisa Khoshvaght","volume":104,"journal":"International Journal of Computer and Information Engineering","pagesStart":1959,"pagesEnd":1965,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10002407","abstract":"In some applications, such as image recognition or\r\ncompression, segmentation refers to the process of partitioning a\r\ndigital image into multiple segments. Image segmentation is typically\r\nused to locate objects and boundaries (lines, curves, etc.) in images.\r\nImage segmentation is to classify or cluster an image into several\r\nparts (regions) according to the feature of image, for example, the\r\npixel value or the frequency response. More precisely, image\r\nsegmentation is the process of assigning a label to every pixel in an\r\nimage such that pixels with the same label share certain visual\r\ncharacteristics. The result of image segmentation is a set of segments\r\nthat collectively cover the entire image, or a set of contours extracted\r\nfrom the image. Several image segmentation algorithms were\r\nproposed to segment an image before recognition or compression. Up\r\nto now, many image segmentation algorithms exist and be\r\nextensively applied in science and daily life. According to their\r\nsegmentation method, we can approximately categorize them into\r\nregion-based segmentation, data clustering, and edge-base\r\nsegmentation. In this paper, we give a study of several popular image\r\nsegmentation algorithms that are available.","references":"[1] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed.,\r\nPrentice Hall, New Jersey 2008.\r\n[2] W. K. Pratt, Digital Image Processing, 3th ed., John Wiley & Sons, Inc.,\r\nLos Altos, California, 2007.\r\n[3] R. Adams, and L. Bischof, \u201cSeeded region growing,\u201d IEEE Trans.\r\nPattern Anal. Machine Intel, vol. 16, no. 6, pp. 641-647, June, 1994.\r\n[4] Z. Lin, J. Jin and H. Talbot, \u201cUnseeded region growing for 3D image\r\nsegmentation,\u201d ACM International Conference Proceeding Series, vol. 9,\r\npp. 31-37, 2000.\r\n[5] S. L. Horowitz and T. Pavlidis, \u201cPicture segmentation by a tree traversal\r\nalgorithm,\u201d JACM, vol. 23, pp. 368-388, April, 1976.\r\n[6] Y. Deng, and B.S. Manjunath, \u201cUnsupervised segmentation of colortexture\r\nregions in images and video,\u201d IEEE Trans. Pattern Anal.\r\nMachine Intel., vol. 23, no. 8, pp. 800-810, Aug. 2001.\r\n[7] Y. Deng, C. Kenney, M.S. Moore, and B.S. Manjunath, \u201cPeer group\r\nfiltering and perceptual color image quantization,\u201d Proc. IEEE Int'l\r\nSymp. Circuits and Systems, vol. 4, pp. 21-24, Jul. 1999.\r\n[8] R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis.\r\nNew York: John Wiley Sons, 1970.\r\n[9] J. J. Ding, C. J. Kuo, and W. C. Hong, \u201cAn efficient image segmentation\r\ntechnique by fast scanning and adaptive merging,\u201d CVGIP, Aug. 2009.\r\n[10] A. K. Jain, M. N. Murty, and P. J. Flynn, \u201cData clustering: a review,\u201d\r\nACM Computing Surveys, vol. 31, issue 3, pp. 264-323, Sep. 1999.\r\n[11] W. B. Frakes and R. Baeza-Yates, Information Retrieval: Data\r\nStructures and Algorithms, Prentice Hall, Upper Saddle River, NJ, 13\u2013\r\n27.\r\n[12] G. Nagy, \u201cState of the art in pattern recognition,\u201d Proc. IEEE, vol. 56,\r\nissue 5, pp. 836\u2013863, May 1968.\r\n[13] A. K. Jain and R.C. Dubes, Algorithms for Clustering Data, Prentice\r\nHall, 1988.\r\n[14] J. MacQueen, Some methods for classification and analysis of\r\nmultivariate observations, In Proceedings of the Fifth Berkeley\r\nSymposium on Mathematical Statistics and Probability, 281\u2013297.\r\n[15] D. Comaniciu and P. Meer, \u201cMean shift: a robust approach toward\r\nfeature space Analysis,\u201d IEEE Trans. Pattern Analysis and Machine\r\nIntelligence, vol. 24, no. 5, pp. 603-619, May 2002.\r\n[16] Y. Cheng, \u201cMean shift, mode seeking, and clustering,\u201d IEEE Trans.\r\nPattern Analysis and Machine Intelligence, vol. 17, no. 8, pp. 790-799,\r\nAug. 1995.\r\n[17] S. C. Pei and J. J. Ding, \u201cThe generalized radial Hilbert transform and its\r\napplications to 2-D edge detection (any direction or specified\r\ndirections),\u201d ICASSP 2003, vol. 3, pp. 357-360, Apr. 2003.\r\n[18] L. Vincent, P. Soille, \u201cWatersheds in digital spaces: an efficient\r\nalgorithm based on immersion simulations,\u201d IEEE Trans. Pattern\r\nAnalysis and Machine Intelligence, vol. 13, issue 6, pp. 583-598, Jun.\r\n1991.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 104, 2015"}