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Two Stream Neural Networks with Traditional CNN and Gabor CNN for Object Classification

Jiakun Li 1 Tian Wang 1 Ming Gao Aichun Zhu 2 Guangcun Shan 3 Hichem Snoussi 4
1 Beihang University
2 SCS - NUIST - School of Computer and Software [Nanjing University]
3 School of Instrumentation Science and Opto-electronics Engineering [Beijing]
4 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
ICD - Institut Charles Delaunay
Abstract : Image classification is still a hot and challenging task in the field of computer vision. With the combination of traditional CNN and Gabor CNN, we designed color-texture convolutional neural networks. We believe that color and texture is the most important feather to describe an object. So the original image, which main consist of color information, and the image processed by Gabor, which contain the texture information, input two stream networks separately is a natural idea. And merging the output of two stream at the end of network to complete the task of classification. We design a simple but effective network structure and test it on Cifar-10, STL-10 and NWPU-RESISC45 dataset. The result shows that the network achieve a well accuracy and proves that our idea is feasible
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Conference papers
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https://hal-utt.archives-ouvertes.fr/hal-02365851
Contributor : Jean-Baptiste Vu Van <>
Submitted on : Friday, November 15, 2019 - 3:27:52 PM
Last modification on : Friday, July 17, 2020 - 9:44:02 PM

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Jiakun Li, Tian Wang, Ming Gao, Aichun Zhu, Guangcun Shan, et al.. Two Stream Neural Networks with Traditional CNN and Gabor CNN for Object Classification. 2018 37th Chinese Control Conference (CCC), Jul 2018, Wuhan, China. pp.9350-9355, ⟨10.23919/ChiCC.2018.8483992⟩. ⟨hal-02365851⟩

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