Support vector machine-based classification of rock texture images aided by efficient feature selection

Changjing Shang, Dave Barnes

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

16 Citations (SciVal)

Abstract

This paper presents a study on rock texture image classification using support vector machines (and also K-nearest neighbours and decision trees) with the aid of feature selection techniques. It offers both unsupervised and supervised methods for feature selection, based on data reliability and information gain ranking respectively. Following this approach, the conventional classifiers which are sensitive to the dimensionality of feature patterns, become effective on classification of images whose pattern representation may otherwise involve a large number of features. The work is successfully applied to complex images. Classifiers built using features selected by either of these methods generally outperform their counterparts that employ the full set of original features which has a dimensionality several folds higher than that of the selected feature subset. This is confirmed by systematic experimental investigations. This study therefore, helps to accomplish challenging image classification tasks effectively and efficiently. In particular, the approach retains the underlying semantics of a selected feature subset. This is very important to ensure that the classification results are understandable by the user.
Original languageEnglish
Title of host publicationThe 2012 International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE Press
Pages1-8
Number of pages8
ISBN (Electronic)978-1-4673-1489-3
ISBN (Print)978-1-4673-1488-6
Publication statusPublished - 2012
Event2012 International Joint Conference on Neural Networks (IJCNN) - Brisbane, Australia
Duration: 10 Jun 201215 Jun 2012

Conference

Conference2012 International Joint Conference on Neural Networks (IJCNN)
Country/TerritoryAustralia
CityBrisbane
Period10 Jun 201215 Jun 2012

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