Improved image set classification via joint sparse approximated nearest subspaces

Chen, Shaokang, Sanderson, Conrad, Harandi, Mehrtash T and Lovell, Brian C. (2013). Improved image set classification via joint sparse approximated nearest subspaces. In: Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on. 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013, Portland, OR United States, (452-459). 23 - 28 June 2013. doi:10.1109/CVPR.2013.65

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Author Chen, Shaokang
Sanderson, Conrad
Harandi, Mehrtash T
Lovell, Brian C.
Title of paper Improved image set classification via joint sparse approximated nearest subspaces
Conference name 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013
Conference location Portland, OR United States
Conference dates 23 - 28 June 2013
Proceedings title Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on   Check publisher's open access policy
Journal name IEEE Conference on Computer Vision and Pattern Recognition. Proceedings   Check publisher's open access policy
Place of Publication Piscataway, NJ United States
Publisher I E E E
Publication Year 2013
Year available 2013
Sub-type Fully published paper
DOI 10.1109/CVPR.2013.65
ISBN 9780769549897
ISSN 1063-6919
Start page 452
End page 459
Total pages 8
Collection year 2014
Language eng
Abstract/Summary 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).
Subjects 1712 Software
1707 Computer Vision and Pattern Recognition
Keyword Adaptive Clustering
Grassmann Manifold
Joint Sparse Representation
Multi-model Image Set Matching
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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