Tampere University of Technology

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Colour and texture based classification of rock images using classifier combinations

Research output: Book/ReportDoctoral thesisCollection of Articles


Original languageEnglish
Place of PublicationTampere
PublisherTampere University of Technology
Number of pages73
ISBN (Electronic)952-15-1819-7
ISBN (Print)952-15-1579-1
Publication statusPublished - 7 Apr 2006
Publication typeG5 Doctoral dissertation (article)

Publication series

NameTampere University of Technology. Publication
PublisherTampere University of Technology
ISSN (Print)1459-2045


The classification of natural images is an essential task in current computer vision and pattern recognition applications. Rock images are a typical example of natural images, and their analysis is of major importance in the rock industry and in bedrock investigations. Rock image classification is based on specific visual descriptors extracted from the images. Using these descriptors, images are divided into classes according to their visual similarity. This thesis investigates rock image classification using two different approaches. Firstly, the colour and texture based description of rock images is developed by applying multiscale texture filtering techniques to the rock images. The emphasis in such image description is to make the filtering for the selected colour channels of the rock images. Additionally, surface reflection images obtained from industrial rock plates are analysed using texture filtering methods. Secondly, the area of image classification is studied in terms of classifier combinations. The purpose of the classifier combination strategies proposed in this thesis is to combine the information provided by different visual descriptors extracted from the image in the classification. This is attained by using separate base classifiers for each descriptor and combining the opinions provided by the base classifiers in the final classification. In this way the texture and colour information of rock images can be combined in the classification to achieve better classification accuracy than a classification using separate descriptors. These methods can be readily applied to automated rock classification in such fields as the rock and stone industry or bedrock investigations.

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