NDLI: Unsupervised learning of stochastic AND-OR templates for object modeling

Content Provider	IEEE Xplore Digital Library
Author	Zhangzhang Si Song-Chun Zhu
Copyright Year	2011
Description	Author affiliation: Deptartment of Statistics, UCLA, USA (Zhangzhang Si; Song-Chun Zhu)
Abstract	This paper presents a framework for unsupervised learning of a hierarchical generative image model called ANDOR Template (AOT) for visual objects. The AOT includes: (1) hierarchical composition as “AND” nodes, (2) deformation of parts as continuous “OR” nodes, and (3) multiple ways of composition as discrete “OR” nodes. These AND/OR nodes form the hierarchical visual dictionary. We show that both the structure and parameters of the AOT model can be learned in an unsupervised way from example images using an information projection principle. The learning algorithm consists two steps: i) a recursive Block-Pursuit procedure to learn the hierarchical dictionary of primitives, parts and objects, which form leaf nodes, AND nodes and structural OR nodes and ii) a Graph-Compression operation to minimize model structure for better generalizability, which produce additional OR nodes across the compositional hierarchy. We investigate the conditions under which the learning algorithm can identify, (i.e. recover) an underlying AOT that generates the data, and evaluate the performance of our learning algorithm through both artificial and real examples.
Starting Page	648
Ending Page	655
File Size	3728115
Page Count	8
File Format	PDF
ISBN	9781467300629
e-ISBN	9781467300636
DOI	10.1109/ICCVW.2011.6130304
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-06
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Visualization Dictionaries Image coding Animals Computational modeling Prototypes
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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