Unsupervised Color-texture Image Segmentation
The measure J in J value segmentation (JSEG) fails to represent the discontinuity of color, which degrades the robustness and discrimination of JSEG. An improved approach for JSEG algorithm was proposed for unsupervised color-texture image segmentation. The texture and photometric invariant edge inf...
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Published in | Shanghai jiao tong da xue xue bao Vol. 13; no. 1; pp. 71 - 75 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Heidelberg
Shanghai Jiaotong University Press
01.02.2008
Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200240, China%Department of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, China |
Subjects | |
Online Access | Get full text |
ISSN | 1007-1172 1995-8188 |
DOI | 10.1007/s12204-008-0071-2 |
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Abstract | The measure J in J value segmentation (JSEG) fails to represent the discontinuity of color, which degrades the robustness and discrimination of JSEG. An improved approach for JSEG algorithm was proposed for unsupervised color-texture image segmentation. The texture and photometric invariant edge information were combined, which results in a discriminative measure for color-texture homogeneity. Based on the image whose pixel values are values of the new measure, region growing-merging algorithm used in JSEG was then employed to segment the image. Finally, experiments on a variety of real color images demonstrate performance improvement due to the proposed method. |
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AbstractList | The measure
J
in
J
value segmentation (JSEG) fails to represent the discontinuity of color, which degrades the robustness and discrimination of JSEG. An improved approach for JSEG algorithm was proposed for unsupervised color-texture image segmentation. The texture and photometric invariant edge information were combined, which results in a discriminative measure for color-texture homogeneity. Based on the image whose pixel values are values of the new measure, region growing-merging algorithm used in JSEG was then employed to segment the image. Finally, experiments on a variety of real color images demonstrate performance improvement due to the proposed method. TP391; The measure J in J value segmentation (JSEG) falls to represent the discontinuity of color, which degrades the robustness and discrimination of JSEG. An improved approach for JSEG algorithm was proposed for unsupervised color-texture image segmentation. The texture and photometric invariant edge information were combined, which results in a discriminative measure for color-texture homogeneity. Based on the image whose pixel values are values of the new measure, region growing-merging algorithm used in JSEG was then employed to segment the image. Finally, experiments on a variety of real color images demonstrate performance improvement due to the proposed method. The measure J in J value segmentation (JSEG) fails to represent the discontinuity of color, which degrades the robustness and discrimination of JSEG. An improved approach for JSEG algorithm was proposed for unsupervised color-texture image segmentation. The texture and photometric invariant edge information were combined, which results in a discriminative measure for color-texture homogeneity. Based on the image whose pixel values are values of the new measure, region growing-merging algorithm used in JSEG was then employed to segment the image. Finally, experiments on a variety of real color images demonstrate performance improvement due to the proposed method. The measure J in J value segmentation (JSEG) fails to represent the discontinuity of color, which degrades the robustness and discrimination of JSEG. An improved approach for JSEG algorithm was proposed for unsupervised color-texture image segmentation. The texture and photometric invariant edge information were combined, which results in a discriminative measure for color-texture homogeneity. Based on the image whose pixel values are values of the new measure, region growing-merging algorithm used in JSEG was then employed to segment the image. Finally, experiments on a variety of real color images demonstrate performance improvement due to the proposed method. |
Author | 郁生阳 张艳 王永刚 杨杰 |
AuthorAffiliation | Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200240, China Department of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, China |
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Keywords | photometric color invariance color-texture segmentation TP 391 edge detection value segmentation (JSEG) region growing J value segmentation (JSEG) |
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References | Shafarenko, Petrou, Kittler (CR3) 1997; 6 Wang, Yang, Zhou (CR7) 2004 Gevers, Smeulders (CR10) 1999; 32 Shi, Malik (CR4) 2000; 22 Van de Weijer, Gevers, Geusebroek (CR11) 2005; 27 Martin, Fowlkes, Tal (CR13) 2001 Zheng, Yang, Zhou (CR8) 2004 Wang (CR2) 1998; 20 Deng, Manjunath (CR6) 2001; 23 Comaniciu, Meer (CR5) 2002; 24 Wyszecki, Stiles (CR12) 1982 Geusebroek, Van den Boomgaard, Smeulders (CR9) 2001; 23 Delignon, Marzouki, Pieczynski (CR1) 1997; 6 J. Shi (71_CR4) 2000; 22 Y. Deng (71_CR6) 2001; 23 J. Weijer Van de (71_CR11) 2005; 27 D. Martin (71_CR13) 2001 L. Shafarenko (71_CR3) 1997; 6 G. Wyszecki (71_CR12) 1982 D. Comaniciu (71_CR5) 2002; 24 J. P. Wang (71_CR2) 1998; 20 J. M. Geusebroek (71_CR9) 2001; 23 T. Gevers (71_CR10) 1999; 32 Y. Wang (71_CR7) 2004 Y. Delignon (71_CR1) 1997; 6 Y. Zheng (71_CR8) 2004 |
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Title | Unsupervised Color-texture Image Segmentation |
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