NDLI: Improved Image Set Classification via Joint Sparse Approximated Nearest Subspaces

Content Provider	IEEE Xplore Digital Library
Author	Shaokang Chen Sanderson, C. Harandi, M.T. Lovell, B.C.
Copyright Year	2013
Description	Author affiliation: Sch. of ITEE, Univ. of Queensland, Brisbane, QLD, Australia (Shaokang Chen; Sanderson, C.; Harandi, M.T.; Lovell, B.C.)
Abstract	Existing multi-model approaches for image set classification extract local models by clustering each image set individually only once, with fixed clusters used for matching with other image sets. However, this may result in the two closest clusters to represent different characteristics of an object, due to different undesirable environmental conditions (such as variations in illumination and pose). To address this problem, we propose to constrain the clustering of each query image set by forcing the clusters to have resemblance to the clusters in the gallery image sets. We first define a Frobenius norm distance between subspaces over Grassmann manifolds based on reconstruction error. We then extract local linear subspaces from a gallery image set via sparse representation. For each local linear subspace, we adaptively construct the corresponding closest subspace from the samples of a probe image set by joint sparse representation. We show that by minimising the sparse representation reconstruction error, we approach the nearest point on a Grassmann manifold. Experiments on Honda, ETH-80 and Cambridge-Gesture datasets show that the proposed method consistently outperforms several other recent techniques, such as Affine Hull based Image Set Distance (AHISD), Sparse Approximated Nearest Points(SANP) and Manifold Discriminant Analysis (MDA).
Starting Page	452
Ending Page	459
File Size	816015
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.65
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manifolds Approximation methods Dictionaries Joints Vectors Image reconstruction Face Multi-model Image Set Matching Joint Sparse Representation Grassmann Manifold Adaptive Clustering
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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