@inproceedings{e291ba6ec9094c568449cb1730fc0436,
title = "Similar trademark image retrieval integrating LBP and convolutional neural network",
abstract = "Trademarks play a very important role in the field of economics and companies and are usually used to distinguish goods among different producers and operators, represent reputation, quality and reliability of firms. In this paper, we utilize convolutional neural network to extract visual features. Then we present a method to extract Uniform LBP features from feature maps of each convolutional layer features based on the pre-trained CNN model. The experiments indicated that the methods we proposed can enhance the robustness of features and solve the drawback of the comparison approach. It is also shown that the methods we proposed get better results in recall, precision and F-Measure in trademark databases including 7139 trademark images and METU trademark database.",
keywords = "Convolutional neural network, Deep learning, LBP, Trademark image retrieval",
author = "Tian Lan and Xiaoyi Feng and Zhaoqiang Xia and Shijie Pan and Jinye Peng",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 9th International Conference on Image and Graphics, ICIG 2017 ; Conference date: 13-09-2017 Through 15-09-2017",
year = "2017",
doi = "10.1007/978-3-319-71598-8\_21",
language = "英语",
isbn = "9783319715971",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "231--242",
editor = "Yao Zhao and Xiangwei Kong and David Taubman",
booktitle = "Image and Graphics - 9th International Conference, ICIG 2017, Revised Selected Papers",
}