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首页> 外文会议>2017 International Conference on Security, Pattern Analysis, and Cybernetics >A novel approach to cloth classification through deep neural networks
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A novel approach to cloth classification through deep neural networks

机译：通过深度神经网络进行布料分类的新方法

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摘要

The recent development of the field of artificial intelligence makes the traditional technical recognition more accurate. An important area is commodity identification which helps to classify commodity and provide information for data-ming and commercial decision. This paper considers cloth classification by means of deep neural networks. We summarize the existing methods: the effect improvement of network can be divided into two kinds, by modifying network structure according to their priorities, i.e., increase the depth of network and enhance the performance of convolution unit. In order to further improve the performance of network model, we redesign the network structure based on AlexNet, and put forward the deep convolution neural network model. Experiments are performed on the data sets including ImageNet-1000 and cloth data sets ACS and CAPB. The results show that the proposed deep convolutional neural network is superior to the original AlexNet on these three data sets in terms of accuracy.
机译：人工智能领域的最新发展使传统技术的认可更加准确。商品识别是一个重要领域，它有助于对商品进行分类并提供信息以进行数据建模和商业决策。本文考虑通过深度神经网络对服装进行分类。我们总结了现有的方法：通过根据网络的优先级修改网络结构可以将网络的效果改进分为两种，即增加网络深度和增强卷积单元的性能。为了进一步提高网络模型的性能，我们重新设计了基于AlexNet的网络结构，并提出了深度卷积神经网络模型。对包括ImageNet-1000和布料数据集ACS和CAPB的数据集进行了实验。结果表明，在这三个数据集上，所提出的深度卷积神经网络在原始数据方面都优于原始的AlexNet。

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