高级检索
朱斌, 杨程, 俞春阳, 安芳. 基于深度学习的产品意象识别[J]. 计算机辅助设计与图形学学报, 2018, 30(9): 1778-1784. DOI: 10.3724/SP.J.1089.2018.16849
引用本文: 朱斌, 杨程, 俞春阳, 安芳. 基于深度学习的产品意象识别[J]. 计算机辅助设计与图形学学报, 2018, 30(9): 1778-1784. DOI: 10.3724/SP.J.1089.2018.16849
Zhu Bin, Yang Cheng, Yu Chunyang, An Fang. Product Image Recognition Based on Deep Learning[J]. Journal of Computer-Aided Design & Computer Graphics, 2018, 30(9): 1778-1784. DOI: 10.3724/SP.J.1089.2018.16849
Citation: Zhu Bin, Yang Cheng, Yu Chunyang, An Fang. Product Image Recognition Based on Deep Learning[J]. Journal of Computer-Aided Design & Computer Graphics, 2018, 30(9): 1778-1784. DOI: 10.3724/SP.J.1089.2018.16849

基于深度学习的产品意象识别

Product Image Recognition Based on Deep Learning

  • 摘要: 为了满足用户对产品的情感化需求,提出一种基于深度学习的产品意象识别方法.该方法通过语义差异法获得产品意象数据集,在此基础上,使用卷积神经网络VGGNet进行训练,建立产品意象深度模型.以典型的椅子产品为例对文中方法进行验证,训练好的产品意象深度模型识别准确率最高可达95.3%.为了进一步证明该方法的优越性,将其分别与以支持向量机(SVM)为代表的传统方法和浅层的卷积神经网络Caffe Net进行对比实验,结果表明,在识别准确率上该方法比SVM提高了约5%,比Caffe Net提升了4%~10%.此外,为了解释深度学习的识别过程,对卷积特征进行了可视化,展现了特征映射从底层到高层的抽象过程.

     

    Abstract: In order to satisfy the users' emotional demands for products, a method based on deep learning was proposed for product image recognition. The method obtained a product image dataset by five-point semantic difference method. VGGNet, a kind of convolutional neural network, was trained with the dataset to establish product image deep model. The typical product of chair was applied to train and verify the product image deep model, which achieved the accuracy up to 93.33%. Furthermore, to prove the superiority of the method, it was compared with the traditional methods using support vector machine (SVM) and shallower convolutional neural network such as CaffeNet. The result shows that the proposed method achieves the accuracy about 5% better than SVM and about 4%-10% than CaffeNet. In addition, convolved features were visualized for explaining the recog- nition progress, demonstrating the abstract progress from low-level to high-level of feature mapping.

     

/

返回文章
返回