Exploiting Hierarchical Context on a Large Database of Object Categories
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Torralba_Exploiting hierarchical.pdf
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4.32 MB
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Author(s)
Choi, Myung Jin
Lim, Joseph Jaewhan
Torralba, Antonio
Willsky, Alan S.
Date Issued
August 2010
Journal
Proceedings of the IEEE Converence on Computer Vision and Pattern Recognition (CVPR), 2010
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Willsky, Alan S. et al. "Exploiting hierarchical context on a large database of object categories." Proceedings of the 2010 IEEE Converence on Computer Vision and Pattern Recognition (CVPR): 129-136. © 2010 IEEE.
Version
Final published version
Abstract
There has been a growing interest in exploiting contextual information in addition to local features to detect and localize multiple object categories in an image. Context models can efficiently rule out some unlikely combinations or locations of objects and guide detectors to produce a semantically coherent interpretation of a scene. However, the performance benefit from using context models has been limited because most of these methods were tested on datasets with only a few object categories, in which most images contain only one or two object categories. In this paper, we introduce a new dataset with images that contain many instances of different object categories and propose an efficient model that captures the contextual information among more than a hundred of object categories. We show that our context model can be applied to scene understanding tasks that local detectors alone cannot solve.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5540221
