Incorporating texture information into region-based unsupervised image segmentation using textural superpixels

Chih Yu Hsu, Yi Yu Hsieh, Kuo Hua Lo, Jen-Hui Chuang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Recently, an unsupervised image segmentation framework, Segmentation by Aggregating Superpixels (SAS) is proposed and shown to be very promising. However, the texture cues, which have been shown to be very effective in many researches, are not used. In this paper, we propose an effective method for incorporating texture information into the SAS framework, using superpixels. To extract texture information, our algorithm first uses texture filtering and subsequently GMM clustering. Then, we develop an edge-aware low-pass filtering to generate multiple-scale textural superpixels (TXSPs) from the clustering results. Finally, by joining TXSPs with the superpixel set originally used in SAS, the incorporation of texture information is accomplished. Our method achieves superior performance on the Berkeley Segmentation Dataset (BSDS300) under several evaluation criteria when compared to other benchmark algorithms.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4323-4327
Number of pages5
ISBN (Electronic)9781479957514
DOIs
StatePublished - 28 Jan 2014

Publication series

Name2014 IEEE International Conference on Image Processing, ICIP 2014

Keywords

  • Superpixel
  • Texture
  • Unsupervised image segmentation

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