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 inShanghai jiao tong da xue xue bao Vol. 13; no. 1; pp. 71 - 75
Main Author 郁生阳 张艳 王永刚 杨杰
Format Journal Article
LanguageEnglish
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
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ISSN1007-1172
1995-8188
DOI10.1007/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.
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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10.1109/TPAMI.2005.75
10.1109/34.977559
10.1109/34.1000236
10.1109/34.868688
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Issue 1
Keywords photometric color invariance
color-texture segmentation
TP 391
edge detection
value segmentation (JSEG)
region growing
J value segmentation (JSEG)
Language English
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Notes photometric color invariance
31-1943/U
color-texture segmentation; J value segmentation (JSEG); photometric color invariance; edge detection; region growing
J value segmentation (JSEG)
color-texture segmentation
edge detection
TP391
region growing
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Snippet The measure J in J value segmentation (JSEG) fails to represent the discontinuity of color, which degrades the robustness and discrimination of JSEG. An...
The measure J in J value segmentation (JSEG) fails to represent the discontinuity of color, which degrades the robustness and discrimination of JSEG. An...
The measure J in J value segmentation (JSEG) fails to represent the discontinuity of color, which degrades the robustness and discrimination of JSEG. An...
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...
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SubjectTerms Architecture
Computer Science
Electrical Engineering
Engineering
Life Sciences
Materials Science
信号识别
图象处理
计算机技术
识别模式
Title Unsupervised Color-texture Image Segmentation
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