Combining Microscopic and Macroscopic Information for Rotation and Histogram Equalization Invariant Texture Classification

Abstract

Abstract. This paper presents a new, simple approach for rotation and histogram equalization invariant texture classification. The proposed approach is based on both microscopic and macroscopic information which can effectively capture fundamental intensity properties of image textures. The combined information is proven to be a very powerful texture feature. We extract the information at the microscopic level by using the frequency histogram of all pattern labels. At the macroscopic level, we extract the information by employing the circular Gabor filters at different center frequencies and computing the Tsallis entropy of the filter outputs. The proposed approach is robust in terms of histogram equalization since the feature is, by definition, invariant against flattening of pixel intensities. The good performance of this approach is proven by the promising experimental results obtained. We also evaluate our method based on six widely used image features. It is experimentally shown that our features exceed the performance obtained using other image features.

Facial Expression Recognition using Advanced Local Binary Patterns, Tsallis Entropies and Global Appearance Features

“…The main contributions of this paper are: (1) The conventional LBP approach is extended to the advanced LBP (ALBP) [8] in both intensity and gradient maps to ensure reliable acquisition of the major pattern information; (2) The Tsallis entropy [9] is used to extract the low-and mid-frequency texture features of the face image; (3) Discriminating global appearance features are extracted by null-space based LDA (NLDA) [10]. Then these three kinds of features are combined to represent the characteristics of the face image.…”