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Optical Engineering

Boundary extraction using supervised edgelet classification
Author(s): Ji Zhao; Jiayi Ma; Jie Ma; Sheng Zheng
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Paper Abstract

Traditional learning-based boundary extraction algorithms classify each pixel edge separately and then get boundaries from the local decisions of a classifier. However, we propose a supervised learning method for boundary extraction by using edgelets as boundary elements. First, we extract edgelets by clustering probabilities of boundary. Second, we use features of edgelets to train a classifier that determines whether an edgelet belongs to a boundary. The classifier is trained by utilizing edgelet features, including local appearance, multiscale features, and global scene features such as saliency maps. Finally, we use the classifier to decide the probability that the edgelet belongs to the boundary. The experimental results in the Berkeley Segmentation Dataset demonstrate that our algorithm can improve the performance of boundary extraction.

Paper Details

Date Published: 6 February 2012
PDF: 8 pages
Opt. Eng. 51(1) 017002 doi: 10.1117/1.OE.51.1.017002
Published in: Optical Engineering Volume 51, Issue 1
Show Author Affiliations
Ji Zhao, Huazhong Univ. of Science and Technology (China)
Jiayi Ma, Huazhong Univ. of Science and Technology (China)
Jie Ma, Huazhong Univ. of Science and Technology (China)
Sheng Zheng, China Three Gorges Univ. (China)

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