Classification in scale-space: Applications to texture analysis
Andrew P. Bradley, PT Jackway, BC Lovell · 1995
this paper we propose a technique for classifying images by modeling features extracted at different scales. Specifically, we use texture measures derived from Pap Smear cell nuclei images using a Grey Level Co-occurrence Matrix (GLCM). For a texture feature extracted from the GLCM at a number of distances we hypothesise that by modeling the feature as a continuous function of scale we can obtain information as to the shape of this function and hence improve its discriminatory power. This hypothesis is compared to the traditional method of selecting a given number of the best single distance measures. It is found, on the limited data set available, that the classification accuracy can be improved by modeling the texture features in this way.