@inproceedings{4a037f803af44063b0a35c2fe4e1daf2,
title = "Two Stream Neural Networks with Traditional CNN and Gabor CNN for Object Classification",
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.",
keywords = "Gabor, Object classification, Texture, Two stream",
author = "Jiakun Li and Tian Wang and Ming Gao and Aichun Zhu and Guangcun Shan and Hichem Snoussi",
note = "Publisher Copyright: {\textcopyright} 2018 Technical Committee on Control Theory, Chinese Association of Automation.; 37th Chinese Control Conference, CCC 2018 ; Conference date: 25-07-2018 Through 27-07-2018",
year = "2018",
month = oct,
day = "5",
doi = "10.23919/ChiCC.2018.8483992",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "9350--9355",
editor = "Xin Chen and Qianchuan Zhao",
booktitle = "Proceedings of the 37th Chinese Control Conference, CCC 2018",
address = "美国",
}