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PatchCut: Data-Driven Object Segmentation via Local Shape Transfer

Title
PatchCut: Data-Driven Object Segmentation via Local Shape Transfer
Author
Yang, Ming-hsuan
Keywords
Shape; Image segmentation; Object segmentation; Yttrium; Image color analysis; Proposals; Databases
Issue Date
2015-06
Publisher
IEEE
Citation
Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on 2015 June, Page. 1770-1778
Abstract
Object segmentation is highly desirable for image understanding and editing. Current interactive tools require a great deal of user effort while automatic methods are usually limited to images of special object categories or with high color contrast. In this paper, we propose a data-driven algorithm that uses examples to break through these limits. As similar objects tend to share similar local shapes, we match query image patches with example images in multiscale to enable local shape transfer. The transferred local shape masks constitute a patch-level segmentation solution space and we thus develop a novel cascade algorithm, PatchCut, for coarse-to-fine object segmentation. In each stage of the cascade, local shape mask candidates are selected to refine the estimated segmentation of the previous stage iteratively with color models. Experimental results on various datasets (Weizmann Horse, Fashionista, Object Discovery and PASCAL) demonstrate the effectiveness and robustness of our algorithm.
URI
http://ieeexplore.ieee.org/document/7298786/?isnumber=7298593&arnumber=7298786http://hdl.handle.net/20.500.11754/25641
ISBN
978-1-4673-6964-0
ISSN
1063-6919
DOI
10.1109/CVPR.2015.7298786
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE AND ENGINEERING(컴퓨터공학부) > Articles
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