Image segmentation based on Support Vector Machine

In content-based multimedia technologies, video object extraction has received more and more attention. In this paper, Support Vector Machine (SVM) is proposed for image segmentation. The SVM is a learning machine algorithm, can reduce the segmentation error which caused by fast motion of the object...

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Published in:2012 2nd International Conference on Computer Science and Network Technology pp. 202 - 206
Main Authors: Wang, Xuejun, Wang, Shuang, Zhu, Yubin, Meng, Xiangyi
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2012
Subjects:
Binary decision tree
Classification algorithms
Discrete cosine transforms
Entropy
Feature extraction
Image edge detection
Image segmentation
Support Vector Machine
Support vector machines
Training
Videos
ISBN:1467329630, 9781467329637
Online Access:Get full text
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Abstract In content-based multimedia technologies, video object extraction has received more and more attention. In this paper, Support Vector Machine (SVM) is proposed for image segmentation. The SVM is a learning machine algorithm, can reduce the segmentation error which caused by fast motion of the object. Firstly, frame difference combined with morphology of mathematics is applied to extract the object roughly. Then, the gray value of image pixels and DCT parameters are computed as the characters of the image for training SVM. Finally, a hierarchical decomposed SVM binary decision tree is used for classification. Experimental results show that the algorithm is effective and robust.
AbstractList In content-based multimedia technologies, video object extraction has received more and more attention. In this paper, Support Vector Machine (SVM) is proposed for image segmentation. The SVM is a learning machine algorithm, can reduce the segmentation error which caused by fast motion of the object. Firstly, frame difference combined with morphology of mathematics is applied to extract the object roughly. Then, the gray value of image pixels and DCT parameters are computed as the characters of the image for training SVM. Finally, a hierarchical decomposed SVM binary decision tree is used for classification. Experimental results show that the algorithm is effective and robust.
Author Meng, Xiangyi
Zhu, Yubin
Wang, Shuang
Wang, Xuejun
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  fullname: Meng, Xiangyi
  organization: College of Communication Engineering, Jilin University, Changchun, China
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Snippet In content-based multimedia technologies, video object extraction has received more and more attention. In this paper, Support Vector Machine (SVM) is proposed...
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StartPage 202
SubjectTerms Binary decision tree
Classification algorithms
Discrete cosine transforms
Entropy
Feature extraction
Image edge detection
Image segmentation
Support Vector Machine
Support vector machines
Training
Videos
Title Image segmentation based on Support Vector Machine
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