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Learning Object Material Categories via Pairwise Discriminant Analysis

Fu, Zhouyu; Robles-Kelly, Antonio

Description

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...[Show more]

dc.contributor.authorFu, Zhouyu
dc.contributor.authorRobles-Kelly, Antonio
dc.coverage.spatialMinneapolis USA
dc.date.accessioned2015-12-10T21:54:08Z
dc.date.createdJune 18-23 2007
dc.identifier.isbn1424411807
dc.identifier.urihttp://hdl.handle.net/1885/38809
dc.description.abstractIn 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.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.relation.ispartofseriesComputer Vision and Pattern Recognition Conference (CVPR 2007)
dc.sourceProceedings of the Computer Vision and Pattern Recognition Conference (CVPR 2007)
dc.source.urihttp://cvpr.cv.ri.cmu.edu/
dc.subjectKeywords: Algorithms; Cost functions; Discriminant analysis; Gradient methods; Learning systems; Problem solving; Hyperspectral imaging; Linear discriminant analysis (LDA); Scatter matrices; Image classification
dc.titleLearning Object Material Categories via Pairwise Discriminant Analysis
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2007
local.identifier.absfor080104 - Computer Vision
local.identifier.ariespublicationu3357961xPUB167
local.type.statusPublished Version
local.contributor.affiliationFu, Zhouyu , College of Engineering and Computer Science, ANU
local.contributor.affiliationRobles-Kelly, Antonio, College of Engineering and Computer Science, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage1
local.bibliographicCitation.lastpage7
local.identifier.doi10.1109/CVPR.2007.383458
dc.date.updated2015-12-09T07:24:09Z
local.identifier.scopusID2-s2.0-35148887223
CollectionsANU Research Publications

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