Computer Vision-Based Color Image Segmentation with Improved Kernel Clustering

1M. Celenk, “A color clustering technique for image segmentation”, Computer Vision, Graphics, and Image Processing, Vol. 52, No. 2, 1990, pp. 145-170.10.1016/0734-189X(90)90052-W Search in Google Scholar

2A. Tremeau, N. Borel, “A region growing and merging algorithm to color segmentation”, Pattern Recognition, Vol. 30, No. 7, 1997, pp. 1191-1203.10.1016/S0031-3203(96)00147-1 Search in Google Scholar

3H. D. Cheng, Y. Sun, “A hierarchical approach to color image segmentation using homogeneity”, IEEE Trans. on Image Processing, Vol. 9, No. 12, 2000, pp. 2071-2082.10.1109/83.887975 Search in Google Scholar

4O. Lezoray, H. Cardot, “Cooperation of color pixel classification schemes and color watershed: a study for microscopic images”, IEEE Transactions on Image Processing, Vol. 11, No. 7, 2002, pp. 783-789.10.1109/TIP.2002.800889 Search in Google Scholar

5L. Shafarenko, M. Petrou, J. Kittler, “Automatic watershed segmentation of randomly textured color images”, IEEE Trans. on Image Processing, Vol. 6, No. 11, 1997, pp. 1530-1544.10.1109/83.641413 Search in Google Scholar

6A. Shiji, N. Hamada, “Color image segmentation method using watershed algorithm and contour information”, International Conference on Image Processing, 1999, pp. 305-309. Search in Google Scholar

7L. H. Ma, Y. Zhang, J. P. Deng, “A target segmentation algorithm based on opening-closing binary marker on watersheds and texture merging”, Journal of Image and Graphics, Vol. 8, 2003, No 1, pp. 77-83. Search in Google Scholar

8G. Celeux, F. Forbes, N. Peyrard, “EM procedures using mean field-like approximations for Markov model-based image segmentation”, Pattern Recognition, Vol. 36, No. 1, 2003, pp.131144.10.1016/S0031-3203(02)00027-4 Search in Google Scholar

9Y. Zhang, M. Brady, S. Smith, “Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm”, IEEE Transactions on Medical Imaging, Vol. 20, No. 1, 2001, pp. 45-57.10.1109/42.906424 Search in Google Scholar

10C. L. Huang, T. Y. Cheng, C. C. Chen, “Color images segmentation using scale space filter and Markov random field”, Pattern Recognition, Vol. 25, No. 10, 1992, pp. 1217-1229.10.1016/0031-3203(92)90023-C Search in Google Scholar

11G. D. Guo, S. Yu, S. D. Ma, “Unsupervised segmentation of color images”, Proc. ICIP, 1998, pp. 299. Search in Google Scholar

12F. Kurugollu, B. Sankur, “Color image segmentation using histogram multi-thresholding and fusion”, Image and Vision Computing, Vol. 19, No. 13, 2001, pp. 915-928.10.1016/S0262-8856(01)00052-X Search in Google Scholar

13C. Q. Liu, H. Cheng, “An efficient clustering method for color image segmentation”, Pattern Recognition and Artificial Intelligence, Vol. 8, No. A01, 1995, pp. 133-138. Search in Google Scholar

14D. Carevic, T. Caelli, “Region-Based Coding of Color Images Using Karhunen-Loeve Transform”, Computer Vision, Graphics, and Image Processing, Vol. 59, No. 1, 1997, pp. 27-38.10.1006/gmip.1996.0402 Search in Google Scholar

15X. W. Wang, L. S. Shen, B. G. Wei, “The focus segmentation of color ophthalmologic image based on modified K-means clustering and mathematical morphology”, Chinese Journal of Biomedical Engineering, Vol. 5, 2002. Search in Google Scholar

16N. Zahid, M. Limouri, A. Esseaid, “A new cluster-validity for fuzzy clustering”, Pattern Recognition, Vol. 32, No. 7, 1999, pp. 1089-1097.10.1016/S0031-3203(98)00157-5 Search in Google Scholar

17K. Y. Lin, L. H. Xu, J. H. Wu, “A fast fuzzy C-means clustering for color image segmentation” , Journal of Image and Graphics, Vol. 9, No. 2, 2004. Search in Google Scholar

18T. Q. Chen, Y. Lu, “Color image segmentation-An innovative approach”, Pattern Recognition, Vol. 35, No. 2, 2002, pp. 395-405.10.1016/S0031-3203(01)00050-4 Search in Google Scholar

19M. Sammouda, R. Sammouda, N. Niki, “Segmentation and analysis of liver cancer pathological color image based on artificial neural networks”, ICIP’99, Vol. 3, 1999, pp. 392-396. Search in Google Scholar

20M. Sammouda, R. Sammouda, “Improving the performance of Hopfield neural network to segment pathological liver color images”, Proceedings of the 17th International Congress and Exhibition, London, June 25-28, 2003.10.1016/S0531-5131(03)00394-7 Search in Google Scholar

21S. H. Ong, N. C. Yeo, K. H. Lee, “Segmentation of color images using a two-stage selforganizing network”, Image and Vision Computing, Vol. 4, 2002.10.1016/S0262-8856(02)00021-5 Search in Google Scholar

22N. Papamarkos, C. Strouthopoulous, and L. Andreadis, “Multithresholding of color and gray-level images through a neural network technique”, Image and Vision Computing, Vol. 18, No. 3, 2000, pp. 213-222.10.1016/S0262-8856(99)00015-3 Search in Google Scholar

23N. K. Suryadevara and S. C. Mukhopadhyay, “Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly”, IEEE Sensors Journal, Vol. 12, No. 6, June 2012, pp. 1965-1972.10.1109/JSEN.2011.2182341 Search in Google Scholar

24N. K. Suryadevara and S. C. Mukhopadhyay, “Determining Wellness Through An Ambient Assisted Living Environment”, IEEE Intelligent Systems, May/June 2014, pp. 30-37.10.1109/MIS.2014.16 Search in Google Scholar

25Rui Xu and D. Wunsch, “Survey of Clustering Algorithms”, IEEE Transaction on Neural Networks, Vol. 16, No. 3, 2005, pp. 645-678.10.1109/TNN.2005.84514115940994 Search in Google Scholar

26J. Han and M. Kamber, Data Mining: Concepts and Techniques, Second Edition, Morgan Kaufmann, San Francisco, 2006. Search in Google Scholar

27M. Filippone, F. Camastra, and F. Masulli, “A Survey of Kernel and Spectral Methods for Clustering”, Pattern Recognition, Vol. 41, No. 1, 2008, pp. 176-190.10.1016/j.patcog.2007.05.018 Search in Google Scholar

28C. J. C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition”, Data Mining and Knowledge Discovery, Vol. 2, No. 2, 1998, pp. 121-167.10.1023/A:1009715923555 Search in Google Scholar

29D. M. J. Tax, R. P. W. Duin, “Support Vector Domain Description”, Pattern Recognition Letters, Vol. 20, No. 11-13, 1999, pp. 1191-1199.10.1016/S0167-8655(99)00087-2 Search in Google Scholar

30A. Ben-Hur, D. Horn, H. T. Siegelmann, V. Vapnik, “Support Vector Clustering”, Journal of Machine Learning Research, Vol. 2, No. 12, 2001, pp. 125-137. Search in Google Scholar

31B. Scholkopf, R. Williamson, A. Smola, J. Shawe-Taylor, J. Platt, “Support Vector Method for Novelty Detection”, Advances in Neural Information Processing System, Vol. 12, 2000, pp. 582-588. Search in Google Scholar

32N. K. Suryadevara, S. C. Mukhopadhyay, R. Wang, R. K. Rayudu, “Forecasting the behavior of an elderly using wireless sensors data in a smart home”, Engineering Applications of Artificial Intelligence, Vol. 26, No. 10, 2013, pp. 2641-2652.10.1016/j.engappai.2013.08.004 Search in Google Scholar

33G. Sen Gupta, S. C. Mukhopadhyay and M. Finnie, “Wi-Fi Based Control of a Robotic Arm with Remote Vision”, Proceedings of 2009 IEEE I2MTC Conference, Singapore, May 5-7, 2009, pp. 557-562.10.1109/IMTC.2009.5168512 Search in Google Scholar

34Yongqing Wang, “NEW INTELLIGENT CLASSIFICATION METHOD BASED ON IMPROVED MEB ALGORITHM”, International Journal on Smart Sensing and Intelligent Systems, Vol. 7, No. 1, 2014, pp. 72-95.10.21307/ijssis-2017-646 Search in Google Scholar

35Yongqing Wang, “FACE RECOGNITION BASED ON IMPROVED SUPPORT VECTOR CLUSTERING”, International Journal on Smart Sensing and Intelligent Systems, Vol. 7, No. 4, 2014, pp. 1807-1829.10.21307/ijssis-2017-734 Search in Google Scholar

36Yongqing Wang, “Simpler Minimum Enclosing Ball: Fast Approximate MEB Algorithm for Extentive Kernel Methods”, CCDC’08, 2008, pp. 3492-3497.10.1109/CCDC.2008.4597996Search in Google Scholar