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    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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    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 Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 20 Jun 2013 09:27
    Last Modified: 11 Oct 2016 15:27
    URI: http://eprints.bbk.ac.uk/id/eprint/7508

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