2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-4.3
Paper Title Gradient Local Binary Pattern for Convolutional Neural Networks
Authors Jialiang Tang, Ning Jiang, Wenxin Yu, Southwest University of Science and Technology, China
SessionMLR-APPL-IP-4: Machine learning for image processing 4
LocationArea D
Session Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
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Abstract 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 3×3 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.