Gradient Local Binary Pattern For Convolutional Neural Networks

Convolutional neural networks(CNNs) have achieved a performance significantly superior to traditional machine learning methods. However, in the traditional machine learning methods, the feature extraction algorithms are compelling and beneficial for CNNs. This paper introduces the classic feature extraction algorithm gradient local binary pattern(GLBP) to the CNNs. More specially, the GLBP extractor weights will be fixed into the $3\times 3$ sized kernels to construct the GLBP layer to replace the first layer of CNNs. In the GLBP layer, the features extracted by the GLBP kernels will concate or add to the feature process by the convolutional kernels. Through extensive experiments, we demonstrated that the GLBP layer could efficiently improve CNNs performance. When training on the ImageNet dataset, the ResNet18 with GLBP layer obtained 1.19% Top-1 accuracy improvement and 0.87% Top-5 accuracy improvement, respectively.