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Optical Engineering

Classification of natural rock images using classifier combinations
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Paper Abstract

Classifier combinations can be used to improve the accuracy of demanding image classification tasks. Using combined classifiers, nonhomogenous images with noisy and overlapping feature distributions can be accurately classified. This can be made by classifying each visual descriptor first individually and combining the separate classification results in a final classification. We present an approach to combine classifiers in image classification. In this method, the probability distributions provided by separate base classifiers are combined into a classification probability vector (CPV) that is used as a feature vector in the final classification. The proposed classifier combination strategy is applied to the classification of natural rock images. The results show that the proposed method outperforms other commonly used probability-based classifier combination strategies in the classification of rock images.

Paper Details

Date Published: 1 September 2006
PDF: 7 pages
Opt. Eng. 45(9) 097201 doi: 10.1117/1.2354086
Published in: Optical Engineering Volume 45, Issue 9
Show Author Affiliations
Leena Lepistö, Tampere Univ. of Technology (Finland)
Iivari Kunttu, Tampere Univ. of Technology (Finland)
Ari J. E. Visa, Tampere Univ. of Technology (Finland)


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