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Feature Learning Based Multi-scale Wavelet Analysis for Textural Image Segmentation

  • Jing Fan
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 168)

Abstract

In order to increase the edge accuracy and the areas consistency, and to reduce the partition error rate in textural image segmentation, we propose a new multi-scale wavelet analysis based on feature learning in this paper. It improves the textural image segmentation by reducing the effect of redundant features on segmentation results. This method includes three stages as follows: feature extraction, optimizing the feature vectors and feature space clustering. In the stage of filtrating valid features, we optimize the feature vectors by feature learning. The experimental results demonstrate that the improved algorithm is effective for textural image segmentation.

Keywords

Texture Image Segmentation Wavelet Transform Feature Learning 

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Jing Fan
    • 1
  1. 1.Department of Mechanical & Electrical EngineeringXi’an University of Arts and ScienceXi’anChina

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