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    Image recognition via two-dimensional random projection and nearest constrained subspace

    Liao, L. and Zhang, Y. and Maybank, Stephen J. and Liu, Z. and Liu, X. (2014) Image recognition via two-dimensional random projection and nearest constrained subspace. Journal of Visual Communication and Image Representation 25 (5), pp. 1187-1198. ISSN 1047-3203.

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    Abstract

    We consider the problem of image recognition via two-dimensional random projection and nearest constrained subspace. First, image features are extracted by a two-dimensional random projection. The two-dimensional random projection for feature extraction is an extension of the 1D compressive sampling technique to 2D and is computationally more efficient than its 1D counterpart and 2D reconstruction is guaranteed. Second, we design a new classifier called NCSC (Nearest Constrained Subspace Classifier) and apply it to image recognition with the 2D features. The proposed classifier is a generalized version of NN (Nearest Neighbor) and NFL (Nearest Feature Line), and it has a close relationship to NS (Nearest Subspace). For large datasets, a fast NCSC, called NCSC-II, is proposed. Experiments on several publicly available image sets show that when well-tuned, NCSC/NCSC-II outperforms its rivals including NN, NFL, NS and the orthonormal ℓ2ℓ2-norm classifier. NCSC/NCSC-II with the 2D random features also shows good classification performance in noisy environment.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): Supervised image classification, Two-dimensional random projection, Compressive sampling, ℓ1ℓ1-norm minimization, ℓ0ℓ0-norm sparse representation, Constrained subspace, Affine hull
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Administrator
    Date Deposited: 08 Apr 2014 09:39
    Last Modified: 19 May 2014 12:28
    URI: http://eprints.bbk.ac.uk/id/eprint/9558

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