A Convolution-Free LBP-HOG Descriptor For Mammogram Classification
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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.
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