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Content Provider | IEEE Xplore Digital Library |
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Author | Long Zhu Yuanhao Chen Yuan Lin Chenxi Lin Yuille, A. |
Copyright Year | 1979 |
Abstract | In this paper, we propose a Hierarchical Image Model (HIM) which parses images to perform segmentation and object recognition. The HIM represents the image recursively by segmentation and recognition templates at multiple levels of the hierarchy. This has advantages for representation, inference, and learning. First, the HIM has a coarse-to-fine representation which is capable of capturing long-range dependency and exploiting different levels of contextual information (similar to how natural language models represent sentence structure in terms of hierarchical representations such as verb and noun phrases). Second, the structure of the HIM allows us to design a rapid inference algorithm, based on dynamic programming, which yields the first polynomial time algorithm for image labeling. Third, we learn the HIM efficiently using machine learning methods from a labeled data set. We demonstrate that the HIM is comparable with the state-of-the-art methods by evaluation on the challenging public MSRC and PASCAL VOC 2007 image data sets. |
Sponsorship | IEEE Computer Society |
Page Count | 13 |
File Size | 1850907 |
Starting Page | 359 |
Ending Page | 371 |
File Format | |
ISSN | 01628828 |
Volume Number | 34 |
Issue Number | 2 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2012-02-01 |
Publisher Place | U.S.A. |
Access Restriction | One Nation One Subscription (ONOS) |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Hierarchical systems Image segmentation Scene analysis scene labeling. Hierarchy parsing segmentation |
Content Type | Text |
Resource Type | Article |
Subject | Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software |
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