A Survey on Image segmentation algorithms

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-35 Number-4
Year of Publication : 2016
Authors : D.Rasi, J.Suganthi
  10.14445/22312803/IJCTT-V35P132

MLA

D.Rasi, J.Suganthi "A Survey on Image segmentation algorithms". International Journal of Computer Trends and Technology (IJCTT) V35(4):170-174, May 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
The applications in image processing like image recognition or compression, the process cannot be done directly due to its inefficiency and practical problems. Hence, some of image segmentation algorithms were introduced to segment an image. Image segmentation is a process of splitting or partitioning an image into multiple numbers of segments that is pixels otherwise known as superpixels. The splitting up of an image into meaningful object is with respect to the similar characteristics like color, intensity, texture etc. Till now various number of image segmentation algorithms were proposed and were applied in our day-to-day life. In general, image segmentation algorithms can be categorized into region-based segmentation, edge-based segmentation, feature based clustering segmentation, threshold based segmentation, graph based segmentation and model based segmentation. The main objective of image segmentation algorithms is to preserve the features of an image with improved efficiency and reduced computational time. We analyze some of the segmentation methodologies that aim at giving better efficiency.

References
[1] R. Adams, and L. Bischof, “Seeded region growing,” IEEE Trans. Pattern Anal. Machine Intell., vol. 16, no. 6, pp. 641-647, June, 1994.
[2] Z. Lin, J. Jin and H. Talbot, “Unseeded region growing for 3D image segmentation,” ACM International Conference Proceeding Series, vol. 9, pp. 31-37, 2000.
[3] S. L. Horowitz and T. Pavlidis, “Picture segmentation by a tree traversal algorithm,” JACM, vol. 23, pp. 368-388, April, 1976.
[4] J. J. Ding, C. J. Kuo, and W. C. Hong, “An efficient image segmentation technique by fast scanning and adaptive merging,” CVGIP, Aug. 2009.
[5]Gang Chen, Tai Hu, Xiaoyong Guo, Xin Meng “A Fast Region-based Image Segmentation Based on Least Square Method”, Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics San Antonio, TX, USA - October 2009
[6] M. Kass, A. Witkin, and D. Terzopoulos. Snakes: Active contour models. Int. J. Comput. Vis., 1:321-331, 1987.
[7] T. Chan and L. Vese. Active contours without edges. IEEE Trans. Imag. Proc., 10:266–277, 2001.
[8] N. Otsu. A threshold selection method from gray-level histograms. IEEE Trans. Sys., Man., Cyber., 9: 62–66,1979
Feature based
[9]Zhensong Chena, Zhiquan Qib, Fan Menga, Limeng Cuic, Yong Shi, “Image Segmentation via Improving Clustering Algorithms with Density and Distance”, Science direct, Elsevier,Procedia Computer Science 55 ( 2015 ) 1015 – 1022.
[10] V. Jumb, M. Sohani, A. Shrivas, Color image segmentation using k-means clustering and otsus adaptive thresholding, Int. J. Innov. Technol. Explor. Eng 3 (9) (2014) 72–76.
[11] Y. Han, P. Shi, An improved ant colony algorithm for fuzzy clustering in image segmentation, Neurocomputing 70 (4) (2007) 665–671.
[12] A. Rodriguez, A. Laio, Clustering by fast search and find of density peaks, Science 344 (6191) (2014) 1492–1496.
[13] K. S. Tan, N. A. M. Isa,W. H. Lim, Color image segmentation using adaptive unsupervised clustering approach, Applied Soft Computing, 13 (4) (2013) 2017–2036.
[14] P.K. Sahoo, S. Soltani, A.K.C. Wong, Y.C. Chen, A survey of thresholding techniques, Computer Vision, Graphics, and Image Processing 41 (1988) 233–260.
[15] J.C. Dunn, A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, Journal of Cybernetics 3 (1973) 32–57.
[16] Xuegang Hu, Lei Li, “Improved fuzzy c-means algorithm for image segmentation”, Journal of Electrical and Electronic Engineering 2015; 3(1): 1-5
[17] S. Krinidis and V. Chatzis, “A robust fuzzy local information C-means clustering algorithm,” IEEE Trans. Image Process., vol. 19, no. 5, pp. 1328-1337, May 2010.
[18]Cannon, R. L., Dave, J. V., & Bezdek, J. C. (1986). Efficient implementation of the fuzzy c-means clustering algorithms. Pattern Analysis and Machine Intelligence, IEEE Transactions on, (2), 248-255.
[19]Kim, D. W., Lee, K. H., & Lee, D. (2004). A novel initialization scheme for the fuzzy c-means algorithm for color clustering. Pattern Recognition Letters, 25(2), 227-237.
[20] J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Kluwer Academic Publishers, 1981.
[21] Xiaoying Jin, Member, IEEE and Curt H. Davis, Senior Member, IEEE ,“ A Genetic Image Segmentation Algorithm with a Fuzzy-Based Evaluation Function”, 2003, The IEEE International Conference on Fuzzy Systems.
[22] D. N. Chun and H. S. Yang, "Robust image segmentation using genetic algorithm with a fuzzy measure," Pattern Recognilion, vol. 29, no. 7,pp. 1195-1211, July 1996.
[23]R. Nevatia and K. R. Babu, "Linear feature extraction and description," Computer Graphics and Image Processing, vol. 13, pp. 257-269, 1980
Edge based
[24] Gomez-Moreno, H., Maldonado-Bascon, S., & Lopez- Ferreras, F.(2001). Edge detection in noisy images using the support vectormachines. IWANN (1) (pp. 685–692).
[25] Y.W. Lim and S.U. Lee, “On the color image segmentationalgorithm based on the thresholding and the fuzzy C-means techniques,” Pattern Recognition, vol. 23, no.9, pp. 935-952, 1990.
[26] Kurugollu, F., Sankur, B., Harmanci, A.E.: „Color image segmentation using histogram multithresholding and fusion‟, Image Vis. Comput., 2001, 19, (13), pp. 915–928 Model based
[27] Lehmann, F.”Turbo segmentation of textured images”, on Pattern Analysis and Machine Intelligence,vol: 33,pp: 16 – 29,2011
[28] J.W. Woods, “Two-Dimensional Discrete Markovian Fields,” IEEETrans. Information Theory, vol. 18, no. 2, pp. 232- 240, Mar. 1972.
[29] R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. John Wiley, New York, 2001. Threshold based
[30] Karthikeyan, B., Vaithiyanathan, V., Venkatraman, B., Menaka, M. ,‟ Analysis of image segmentation for radiographic images‟ in Indian Journal of Science and Technology 5 (11) , pp. 3660-3664

Keywords
Image Segmentation, Region Growing, Cluster Centroids, Genetic Algorithm.