Two dimensional principal components of natural images and its application

Authors:
Dong Wang;Huchuan Lu;Xuelong Li
Affiliations:
School of Information and Communication Engineering, Dalian University of Technology, China;School of Information and Communication Engineering, Dalian University of Technology, China;School of Information and Communication Engineering, Dalian University of Technology, China and Xi'an Institute of Optics and Precision Mechanics Of CAS, China
Venue:
Neurocomputing
Year:
2011

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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.