NDLI: Omni-range spatial contexts for visual classification

Content Provider	IEEE Xplore Digital Library
Author	Bingbing Ni Mengdi Xu Jinhui Tang Shuicheng Yan Moulin, P.
Copyright Year	2012
Description	Author affiliation: Advanced Digital Sciences Center, Singapore (Bingbing Ni) \|\| Nanjing University of Science and Technology, China (Jinhui Tang) \|\| National University of Singapore, Singapore (Mengdi Xu; Shuicheng Yan) \|\| University of Illinois at Urbana-Champaign, USA (Moulin, P.)
Abstract	Spatial contexts encode rich discriminative information for visual classification. However, as object shapes and scales vary significantly among images, spatial contexts with manually specified distance ranges are not guaranteed with optimality. In this work, we investigate how to automatically select discriminative and stable distance bin groups for modeling image spatial contexts to improve classification performance. We make two observations. First, the number of distance bins for context modeling can be arbitrarily large, and discriminative contexts are only from a small subset of distance bins. Second, adjacent distance bins for contexts modeling often show similar characteristics, thus encouraging grouping them together can result in more stable representation. Utilizing these two observations, we propose an omni-range spatial context mining framework for image classification. A sparse selection and grouping regularizer is employed along with an empirical risk, to discover discriminative and stable distance bin groups for context modeling. To facilitate efficient optimization, the objective function is approximated by a smooth convex function with theoretically guaranteed error bounds. The selected and grouped image spatial contexts, which are applied in food and national flag recognition, are demonstrated to be discriminative, compact and robust.
Starting Page	3514
Ending Page	3521
File Size	566423
Page Count	8
File Format	PDF
ISBN	9781467312264
ISSN	10636919
e-ISBN	9781467312288
e-ISBN	9781467312271
DOI	10.1109/CVPR.2012.6248094
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Visualization Context modeling Approximation methods Histograms Merging Vectors
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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