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Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

Li, Zhengrong, Liu, Yuee, Hayward, Ross F., & Walker, Rodney A. (2010) Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification. In Proceedings of 2010 IEEE 17th International Conference on Image Processing, IEEE, Hong Kong.

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Abstract

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

Impact and interest:

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ID Code: 39275
Item Type: Conference Paper
Keywords: color-texture feature fusion, geographic object-based image analysis (GEOBIA), kernel principal component analysis, local binary patters, vegetation classification
DOI: 10.1109/ICIP.2010.5652028
ISBN: 9781424479924
ISSN: 1522-4880
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Current > Research Centres > Australian Research Centre for Aerospace Automation
Past > Schools > Computer Science
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 2010 IEEE
Deposited On: 20 Dec 2010 13:28
Last Modified: 22 Jun 2011 01:02

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