An Unsupervised Segmentation Framework For Texture Image Queries

نویسندگان

  • Shu-Ching Chen
  • Chengcui Zhang
  • Mei-Ling Shyu
چکیده

In this paper, a novel unsupervised segmentation framework for texture image queries is presented. The proposed framework consists of an unsupervised segmentation method for texture images, and a multi-filter query strategy. By applying the unsupervised segmentation method on each texture image, a set of texture feature parameters for that texture image can be extracted automatically. Based upon these parameters, an effective multi-filter query strategy which allows the users to issue texture-based image queries is developed. The test results of the proposed framework on 318 texture images obtained from the MIT VisTex and Brodatz database are presented to show its effectiveness.

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تاریخ انتشار 2001