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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Official URL: http://dx.doi.org/10.1016/j.jvcir.2014.03.007

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 Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Depositing User:	Administrator
Date Deposited:	08 Apr 2014 09:39
Last Modified:	09 Aug 2023 12:34
URI:	https://eprints.bbk.ac.uk/id/eprint/9558

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