Dictionary learning and sparse coding on Grassmann manifolds : an extrinsic solution

Harandi, M., Sanderson, C., Shen, Chunhua, & Lovell, B. (2013) Dictionary learning and sparse coding on Grassmann manifolds : an extrinsic solution. In Proceedings 2013 IEEE International Conference on Computer Vision (ICCV), The Institute of Electrical and Electronics Engineers, Inc., Sydney Convention and Exhibition Centre, Sydney, Australia, pp. 3120-3127.

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Abstract

Recent advances in computer vision and machine learning suggest that a wide range of problems can be addressed more appropriately by considering non-Euclidean geometry. In this paper we explore sparse dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds. To this end, we propose to embed Grassmann manifolds into the space of symmetric matrices by an isometric mapping, which enables us to devise a closed-form solution for updating a Grassmann dictionary, atom by atom. Furthermore, to handle non-linearity in data, we propose a kernelised version of the dictionary learning algorithm. Experiments on several classification tasks (face recognition, action recognition, dynamic texture classification) show that the proposed approach achieves considerable improvements in discrimination accuracy, in comparison to state-of-the-art methods such as kernelised Affine Hull Method and graph-embedding Grassmann discriminant analysis.

Impact and interest:

29 citations in Scopus
11 citations in Web of Science®
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ID Code: 71634
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
DOI: 10.1109/ICCV.2013.387
ISBN: 9781479928392
ISSN: 1550-5499
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > APPLIED MATHEMATICS (010200)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 The Institute of Electrical and Electronics Engineers, Inc.
Copyright Statement: © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
Deposited On: 14 May 2014 22:31
Last Modified: 16 May 2014 08:00

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