A Convolution-Free LBP-HOG Descriptor For Mammogram Classification

In image based feature descriptor design, an iterative scanning process utilizing the convolution operation is often adopted to extract local information of the image pixels. In this paper, we propose a convolution-free Local Binary Pattern (CF-LBP) and a convolution-free Histogram of Oriented Gradients (CF-HOG) descriptors in matrix form for mammogram classification. An integrated form of CF-LBP and CF-HOG, CF-LBP-HOG, is subsequently constructed in a single matrix formulation. The proposed descriptors are evaluated using a publicly available mammogram database. The results show promising performance in terms of classification accuracy and computational efficiency.