NDLI: Unsupervised Learning of Dictionaries of Hierarchical Compositional Models

Content Provider	IEEE Xplore Digital Library
Author	Jifeng Dai Yi Hong Wenze Hu Song-Chun Zhu Ying Nian Wu
Copyright Year	2014
Description	Author affiliation: WalmartLab, USA (Yi Hong) \|\| Tsinghua Univ., Beijing, China (Jifeng Dai) \|\| Google Inc., Mountain View, CA, USA (Wenze Hu) \|\| Univ. of California Los Angeles, Los Angeles, CA, USA (Song-Chun Zhu; Ying Nian Wu)
Abstract	This paper proposes an unsupervised method for learning dictionaries of hierarchical compositional models for representing natural images. Each model is in the form of a template that consists of a small group of part templates that are allowed to shift their locations and orientations relative to each other, and each part template is in turn a composition of Gabor wavelets that are also allowed to shift their locations and orientations relative to each other. Given a set of unannotated training images, a dictionary of such hierarchical templates are learned so that each training image can be represented by a small number of templates that are spatially translated, rotated and scaled versions of the templates in the learned dictionary. The learning algorithm iterates between the following two steps: (1) Image encoding by a template matching pursuit process that involves a bottom-up template matching sub-process and a top-down template localization sub-process. (2) Dictionary re-learning by a shared matching pursuit process. Experimental results show that the proposed approach is capable of learning meaningful templates, and the learned templates are useful for tasks such as domain adaption and image cosegmentation.
Starting Page	2505
Ending Page	2512
File Size	1273354
Page Count	8
File Format	PDF
ISBN	9781479951185
ISSN	10636919
DOI	10.1109/CVPR.2014.321
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Matching pursuit algorithms Visualization Computational modeling Image coding Unsupervised learning Training
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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