Supervised texture classification — Selection of moment lags

This papers deals with supervised texture classification. The extracted features are the image second and third order moments. The number of possible moment lags for 2-D signals increases rapidly with the order of the moment even for small lag neighbourhoods. The paper focuses on the selection of moment lags that optimise classification performance. Lag selection also serves another purpose: it waives us from the trouble of calculating a large number of moments every time a new sample is to be classified. Lag selection is performed by a full stepwise feature selection method using four different feature evaluation measures. The selected moments are driven to four classifiers and comparative classification results are obtained.