A new framework of unsupervised image segmentation with weight optimization

International Journal of Innovative Computing, Information and Control

Volumn 6, Issue 7, 2010, Pages 3289-3297

  Deng, Xiaoling ^a,b   Hong, Tiansheng ^b   Jiang, Xiaohua ^a   Li, Zhen ^c  

^a SUN YAT SEN UNIVERSITY   (China)

^b SOUTH CHINA AGRICULTURAL UNIVERSITY   (China)

^c NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Color texture image segmentation; Weight optimization

Indexed keywords

COLOR AND TEXTURE FEATURES; COLOR FEATURES; COMBINED FEATURES; FEATURE WEIGHT; FEATURE WEIGHTING; IMAGE SEGMENTATION ALGORITHM; OPTIMAL WEIGHT; OPTIMIZATION PROCESS; SEGMENTATION RESULTS; SEGMENTED REGIONS; SIMILARITY MEASURE; TEXTURE FEATURES; TEXTURE IMAGE SEGMENTATION; UNSUPERVISED IMAGE SEGMENTATION; WEIGHT OPTIMIZATION; WEIGHT VECTOR;

COLOR; DIGITAL IMAGE STORAGE; OPTIMIZATION; PATTERN RECOGNITION; TEXTURES; WEIGHING;

IMAGE SEGMENTATION;

EID: 77955192677     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (17)

1. J. S. Taur, G. H. Lee, C. W. Tao, C. C. Chen and C. W. Yang, Segmentation of psoriasis vulgaris images using multiresolution-based orthogonal subspace techniques, IEEE Transactions on Systems, Man, and Cybernetics, Part B, 36, 2, 390-402, 2006.
2. J. Shi and J. Malik, Normalized cuts and image segmentation, IEEE Trans. Pattern Anal. Mach. Intell, 22, 8, 888C905, 2000.
3. W.-Y. Ma and B. S. Manjunath, Edgeflow: A technique for boundary detection and image segmentation, IEEE Transactions on Image Processing, 1375-1388, 2000.
4. R. Baldrich, R. Toledo, E. Valveny and M. Vanrell, Perceptual colour image segmentation, The 2nd IASTED International Conference on Visualization, Imaging and, Image Processing, Malaga, Spain, 2002.
5. S. Makrogiannis, G. Economou and S. Fotopoulos, A region dissimilarity relation that combines feature-space and spatial information for color image segmentation, IEEE Transactions on Systems, Man, and Cybernetics, Part B, 35, l, 44-53, 2005.
6. W. Tao, H. Jin and Y. Zhang, Color image segmentation based on mean shift and normalized cuts, IEEE Transactions on Systems, Man, and Cybernetics, Part B, 37, 5, 1382-1389, 2007.
7. L. Wu and X. Meng, A robust object segmentation method, International Journal of Innovative Computing, Information and Control, 4, 11, 2008.
8. Y. Aoki and T. Kato, A new segmentation method in SAR image reconstruction, International Journal of Innovative Computing, Information and Control, 3, 2, 235-245, 2007.
9. Y. Itai, H. Kim, S. Ishikawa, A. Yamamoto and K. Nakamura, A segmentation method of lung areas by using snakes and automatic detection of abnormal shadow on the areas, International, Journal, of Innovative Computing, Information and Control, 3, 2, 277-284, 2007.
10. M. Saitou, A. Kojima, T. Kitahashi and K. Fukunaga, Dynamic recognition of human actions and objects using dual hierarchical models, International Journal of Innovative Computing, Information and Control, 3, 6(A), 1359-1368, 2007.
11. A. C. Bovik, Analysis of multichannel narrowband filters for image texture segmentation, IEEE Trans. Signal Processing, 39, 2025-2043, 1991.
12. Z. Yu and II. S. Wong, FEMA: A fast expectation maximization algorithm based on grid and PCA, IEEE International Conference on Multimedia & Expo, 1913-1916, 2006.
13. S. Chiu, Fuzzy model identification based on cluster estimation, Journal of Intelligent & Fuzzy Systems, 2, 3, 267-278, 1994.
14. R. Unnikrishnan, C. Pantofaru and M. Hebert, Toward objective evaluation of image segmentation algorithms, IEEE Transactions on Pattern Analysis and, Machine Intelligence, 29, 6, 929-944, 2007.
15. M. Srinivas and L. M. Patnaik, Genetic algorithms: A survey, IEEE Comput., 27, 6, 17-26, 1994.
16. H. J. A. M. Heijmans, Morphological Image Operators, Academic Press, Boston, 1994.
17. http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds.