Improved image set classification via joint sparse approximated nearest subspaces

Shaokang Chen, Conrad Sanderson, Mehrtash T. Harandi, Brian C. Lovell

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76 Citations (Scopus)


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).

Original languageEnglish
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE Computer Society
Number of pages8
Publication statusPublished - 15 Nov 2013
Externally publishedYes
EventIEEE Conference on Computer Vision and Pattern Recognition 2013 - Portland, United States of America
Duration: 23 Jun 201328 Jun 2013 (Proceedings)


ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2013
Abbreviated titleCVPR 2013
Country/TerritoryUnited States of America
Internet address


  • Adaptive Clustering
  • Grassmann Manifold
  • Joint Sparse Representation
  • Multi-model Image Set Matching

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