Texture Classification using Non-Parametric Markov Random Fields

This thesis investigates texture classification using Non-Parametric Markov Random fields. Texture models using local image descriptors are investigated. Classification performance using such models is then reported upon and the results are used to guide development of future classifiers which take account of scale information within an image. The issues and effects of scale within texture modelling and classification are explored. From this investigation texture models which incorporate scalar information are developed. Results are presented upon these classifiers and the reasons behind them are analysed. The use of region detectors within texture classification is investigated and its role questioned. Finaly an investigation of the role that the zooming level plays within texture is investigated.