Contour detection via random forest

Chao Zhang, Xiang Ruan, Yuming Zhao, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)


Contour detection is an important and fundamental problem in computer vision that finds numerous applications. In this paper, we propose a learning algorithm for contour detection via random forest. Visual cues that can be extracted easily and efficiently are integrated to learn a detector where the decision of an contour pixel is made independently via the random forest at each location in the image. We evaluate the proposed algorithm against leading methods in the literature on the Berkeley Segmentation Dataset. Experimental results demonstrate that the proposed contour detection algorithm performs favorably against state-of-the-art methods in terms of speed and accuracy.

Original languageEnglish
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Number of pages4
Publication statusPublished - 2012
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: 2012 Nov 112012 Nov 15

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference21st International Conference on Pattern Recognition, ICPR 2012

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


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