Learning Object Material Categories via Pairwise Discriminant Analysis

In this paper, we investigate linear discriminant analysis (LDA) methods for multiclass classification problems in hyperspectral imaging. We note that LDA does not consider pairwise relations between different classes, it rather assumes equal within and between-class scatter matrices. As a result, w...

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Bibliographic Details
Published in2007 IEEE Conference on Computer Vision and Pattern Recognition pp. 1 - 7
Main Authors Zhouyu Fu, Robles-Kelly, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2007
Subjects
Algorithm design and analysis
Australia
Cost function
Feature extraction
Hyperspectral imaging
Image analysis
Linear discriminant analysis
Optimization methods
Scattering
Space technology
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Summary:In this paper, we investigate linear discriminant analysis (LDA) methods for multiclass classification problems in hyperspectral imaging. We note that LDA does not consider pairwise relations between different classes, it rather assumes equal within and between-class scatter matrices. As a result, we present a pairwise discriminant analysis algorithm for learning class categories. Our pairwise linear discriminant analysis measures the separability of two classes making use of the class centroids and variances. Our approach is based upon a novel cost function with unitary constraints based on the aggregation of pairwise costs for binary classes. We view the minimisation of this cost function as an unconstrained optimisation problem over a Grassmann manifold and solve using a projected gradient method. Our approach does not require matrix inversion operations and, therefore, does not suffer of stability problems for small training sets. We demonstrate the utility of our algorithm for purposes of learning material catergories in hyperspectral images.
ISBN:9781424411795
1424411793
ISSN:1063-6919
DOI:10.1109/CVPR.2007.383458