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Activity Number: 647
Type: Topic Contributed
Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
Sponsor: International Indian Statistical Association
Abstract - #305714
Title: Spatially Weighted Principal Component Analysis for Image Classification
Author(s): Ruixin Guo*+ and Hongtu Zhu
Companies: University of Colorado and The University of North Carolina at Chapel Hill
Address: , , , United States
Keywords: PCA ; Image classification

Two challenges of classification for image data are the high dimensionality of the feature space and the complex spatial structure on a two-dimensional (2D) surface or in a 3D volume. Thus, dimension reduction prior to classification becomes important and necessary for image data. However, commonly used dimension reduction methods do not take into account of the underlying spatial information possessed by image data. This work develops a Spatially Weighted Principal Component Analysis (SWPCA) method by introducing two sets of weights (global and local spatial weights) to the reconstruction error, which enables a selective treatment of individual features and has the ability of incorporating not only the class label information but also the local spatial pattern of image data. A two-step iterative algorithm is provided for SWPCA, along with proposed weight determination procedures. We apply our method to both simulation studies and a real data analysis. Our proposed SWPCA demonstrates substantial improvement over commonly used PCA methods based on the classification results.

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