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Green Image
Issue No. 02 - February (2012 vol. 34)
ISSN: 0162-8828
pp: 359-371
Long Zhu , Univ. of California, Los Angeles, Los Angeles, CA, USA
Yuanhao Chen , Univ. of California, Los Angeles, Los Angeles, CA, USA
Yuan Lin , Shanghai Jiaotong Univ., Shanghai, China
Chenxi Lin , Alibaba Group R&D, Beijing, China
A. Yuille , Univ. of California, Los Angeles, Los Angeles, CA, USA
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.
polynomials, context-free grammars, dynamic programming, image segmentation, inference mechanisms, learning (artificial intelligence), object recognition, coarse-to-fine representation, HIM, image recursive segmentation, object recognition templates, hierarchical image model, image parsing, contextual information, natural language models, sentence structure, hierarchical representation, rapid inference algorithm, dynamic programming, polynomial time algorithm, image labeling, machine learning methods, labeled data set, public MSRC image data sets, PASCAL VOC 2007 image data sets, Hierarchical systems, Image segmentation, Scene analysis, scene labeling., Hierarchy, parsing, segmentation

Long Zhu, Yuanhao Chen, Yuan Lin, A. Yuille and Chenxi Lin, "Recursive segmentation and recognition templates for image parsing," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 359-371, 2012.
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