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A pixel-based color image segmentation using support vector machine and fuzzy C-means.

Xiang-Yang Wang1, Xian-Jin Zhang, Hong-Ying Yang

  • 1School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China. wxy37@126.com

Neural Networks : the Official Journal of the International Neural Network Society
|June 1, 2012
PubMed
Summary

This study introduces a novel pixel-based color image segmentation method using Support Vector Machine (SVM) and Fuzzy C-Means (FCM). The approach enhances segmentation quality and reduces processing time compared to existing techniques.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Image segmentation is crucial for simplifying complex image data.
  • Existing methods often require significant computational resources and time.
  • Advanced feature extraction is needed for accurate segmentation.

Purpose of the Study:

  • To develop an efficient pixel-based color image segmentation technique.
  • To integrate Support Vector Machine (SVM) and Fuzzy C-Means (FCM) for improved segmentation.
  • To enhance the quality and reduce the processing time of image segmentation.

Main Methods:

  • Pixel-level color and texture features were extracted using local spatial similarity and Steerable filters.
  • A Support Vector Machine (SVM) classifier was trained using Fuzzy C-Means (FCM) with extracted features.
  • The trained SVM model was applied for the final color image segmentation.

Main Results:

  • The proposed method effectively utilizes local image information and SVM classification capabilities.
  • Experimental results demonstrate superior computational behavior and effectiveness.
  • Significant reductions in segmentation time and improvements in segmentation quality were observed.

Conclusions:

  • The combined SVM and FCM approach offers an effective solution for color image segmentation.
  • This method outperforms recent state-of-the-art segmentation techniques in terms of speed and quality.
  • The technique holds promise for various image processing applications requiring efficient segmentation.