DocumentCode :
3672141
Title :
Complexity-adaptive distance metric for object proposals generation
Author :
Yao Xiao; Cewu Lu;Efstratios Tsougenis; Yongyi Lu; Chi-Keung Tang
Author_Institution :
The Hong Kong University of Science and Technology, China
fYear :
2015
fDate :
6/1/2015 12:00:00 AM
Firstpage :
778
Lastpage :
786
Abstract :
Distance metric plays a key role in grouping superpixels to produce object proposals for object detection. We observe that existing distance metrics work primarily for low complexity cases. In this paper, we develop a novel distance metric for grouping two superpixels in high-complexity scenarios. Combining them, a complexity-adaptive distance measure is produced that achieves improved grouping in different levels of complexity. Our extensive experimentation shows that our method can achieve good results in the PASCAL VOC 2012 dataset surpassing the latest state-of-the-art methods.
Keywords :
"Complexity theory","Proposals","Image color analysis","Measurement","Image segmentation","Merging","Tin"
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on
Electronic_ISBN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2015.7298678
Filename :
7298678
Link To Document :
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