Classification of natural images using supervised and unsupervised classifier combinations

Authors:
Leena Lepistö;Iivari Kunttu;Jorma Autio;Ari Visa
Affiliations:
Institute of Signal Processing, Tampere University of Technology, Tampere, Finland;Institute of Signal Processing, Tampere University of Technology, Tampere, Finland;Saanio & Riekkola Consulting Engineers, Helsinki, Finland;Institute of Signal Processing, Tampere University of Technology, Tampere, Finland
Venue:
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Year:
2005

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Abstract

Combining classifiers has proved to be an effective solution to several classification problems in pattern recognition. In this paper we use classifier combination methods for the classification of natural images. In the image classification, it is often beneficial to consider each feature type separately, and combine the classification results in the final classifier. We present a classifier combination strategy that is based on classification result vector, CRV. It can be applied both in supervised and unsupervised manner. In this paper we apply our classifier combination method to the classification of rock images that are non-homogenous in terms of their color and texture properties.