Genetic programming-based fusion of HOG and LBP features for fully automated texture classification

Classifying texture images relies heavily on the quality of the extracted features. However, producing a reliable set of features is a difficult task that often requires human intervention to select a set of prominent primitives. The process becomes more difficult when it comes to fuse low-level descriptors because of data redundancy and high dimensionality. To overcome these challenges, several approaches use machine learning to automate primitive detection and feature extraction while combining low-level descriptors. Nevertheless, most of these approaches performed the two processes separately while ignoring the correlation between them. In this paper, we propose a genetic programming (GP)-based method that combines the two well-known features of histograms of oriented gradients and local binary patterns. Indeed, a three-layer tree-based binary program is learned using genetic programming for each pair of classes. The three layers incorporate patch detection, feature fusion and classification in the GP optimization process. The feature fusion function is designed to handle different variations, notably illumination and rotation, while reducing dimensionality. The proposed method has been compared, using six challenging collections of images, with multiple domain-expert GP and non-GP methods for binary and multi-class classifications. Results show that the proposed method significantly outperforms or achieves similar performance to relevant methods from the state-of-the-art, even with a limited number of training instances.