A quantitative evaluation of texture feature robustness and interpolation behaviour

Abstract

Whenever an image database has to be organised according to higher level human perceptual properties, a transformation model is needed to bridge the semantic gap between features and the perceptual space. To guide the feature selection process for a transformation model, we investigate the behaviour of 5 texture feature categories. Using a novel mixed synthesis algorithm we generate textures with a gradual transition between two existing ones, to investigate the feature interpolation behaviour. In addition the features' robustness to minor textural changes is evaluated in a kNN query-by-example experiment. We compare robustness and interpolation behaviour, showing that Gabor energy map features are outperforming gray level co-occurrence matrix features in terms of linear interpolation quality. © 2009 Springer Berlin Heidelberg.