Patents Images Retrieval and Convolutional Neural Network Training Dataset Quality Improvement

被引：0

作者：

Kravets, Alla G. ^{[1
]}

Lebedev, Nikita ^{[1
]}

Legenchenko, Maxim ^{[1
]}

机构：

[1] Volgograd State Tech Univ, CAD Dept, Volgograd, Russia

来源：

PROCEEDINGS OF THE IV INTERNATIONAL RESEARCH CONFERENCE INFORMATION TECHNOLOGIES IN SCIENCE, MANAGEMENT, SOCIAL SPHERE AND MEDICINE (ITSMSSM 2017) | 2017年 / 72卷

关键词：

patent image; neural network; formation dataset; training dataset quality; deep learning; patents images retrieval; convolutional neural network;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The paper considers the problem of the analysis of patents' figures for formalization of subjective opinions of the patent office experts that reviews applications for inventions. Drawings omission may indicate an incomplete description of the invention and entail the rejection of patent applications and other problems. Since patent images, even if one considers images of the same type, class, etc., are unique, different from each other. Nowadays for image processing are applied neural networks with different architectures. Neural network, Convolutional neural network, Siamese neural network were considered in the research. 4 libraries (Theano, TensorFlow, Caffe, and Keras) were studied. The main contributions of the paper are the new classification of patents' imaged, training dataset formation and quality improvement approach, and the software implementation for CNN training.

引用

页码：287 / 293

页数：7

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