Two dimensional principal components of natural images and its application

Wang, D. and Lu, H. and Li, Xuelong (2011) Two dimensional principal components of natural images and its application. Neurocomputing 74 (17), pp. 2745-2753. ISSN 0925-2312.

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Official URL: http://dx.doi.org/10.1016/j.neucom.2011.03.047

Abstract

In this paper, two dimensional principal components of natural images (2D-PCs) are proposed. Similar to principal components of natural images (1D-PCs), 2D-PCs can also be viewed as fundamental components of human's receptive field because they contain edge-like, bar-like and grating-like patterns. However, compared to 1D-PCs, 2D-PCs are of surprising symmetry, stable regularity, good interpretability, and have little computational complexity in real applications. Then, based on 1D-PCs and 2D-PCs, we design two kinds of statistical texture features (STF(1D) and STF(2D)), and apply them to multi-class facial expression recognition. Numerous experimental results demonstrate that our statistical texture features are better or not worse than other popular features for facial expression recognition.

Metadata

Item Type:	Article
Keyword(s) / Subject(s):	PCA, 2DPCA, principal components, two dimensional principal components, natural images
School:	Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Depositing User:	Sarah Hall
Date Deposited:	20 Jun 2013 09:27
Last Modified:	09 Aug 2023 12:33
URI:	https://eprints.bbk.ac.uk/id/eprint/7508

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