Contour and texture analysis for image segmentation

Research output: Contribution to journalJournal articleResearchpeer-review

This paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent brightness and texture. Natural images contain both textured and untextured regions, so the cues of contour and texture differences are exploited simultaneously. Contours are treated in the intervening contour framework, while texture is analyzed using textons. Each of these cues has a domain of applicability, so to facilitate cue combination we introduce a gating operator based on the texturedness of the neighborhood at a pixel. Having obtained a local measure of how likely two nearby pixels are to belong to the same region, we use the spectral graph theoretic framework of normalized cuts to find partitions of the image into regions of coherent texture and brightness. Experimental results on a wide range of images are shown.

Original languageEnglish
JournalInternational Journal of Computer Vision
Volume43
Issue number1
Pages (from-to)7-27
Number of pages21
ISSN0920-5691
DOIs
Publication statusPublished - Jun 2001
Externally publishedYes

Bibliographical note

Funding Information:
The authors would like to thank the Berkeley vision group, especially Chad Carson, Alyosha Efros, David Forsyth, and Yair Weiss for useful discussions during the development of the algorithm. We thank Doron Tal for implementing the algorithm in C++. This research was supported by (ARO) DAAH04-96-1-0341, the Digital Library Grant IRI-9411334, NSF Graduate Fellowships to SB and JS and a Berkeley Fellowship to TL.

    Research areas

  • Cue integration, Grouping, Normalized cut, Segmentation, Texton, Texture

ID: 302058963