? 基于局部加强PCAnet神经网络的细粒度车型识别方法
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Journal of Computer Science and Technology 2018, Vol. 33 Issue (2) :335-350    DOI: 10.1007/s11390-018-1822-7
Artificial Intelligence and Pattern Recognition << Previous Articles | Next Articles >>
基于局部加强PCAnet神经网络的细粒度车型识别方法
Qian Wang1,2,3, You-Dong Ding3,4, Senior Member, CCF
1 School of Computer Engineering and Science, Shanghai University, Shanghai 200072, China;
2 Information Center, Criminal Investigation Department of Shanghai Public Security Bureau, Shanghai 200083, China;
3 Shanghai Engineering Research Center of Motion Picture Special Effects, Shanghai 200072, China;
4 Shanghai Film Academy, Shanghai University, Shanghai 200072, China
A Novel Fine-Grained Method for Vehicle Type Recognition Based on the Locally Enhanced PCANet Neural Network
Qian Wang1,2,3, You-Dong Ding3,4, Senior Member, CCF
1 School of Computer Engineering and Science, Shanghai University, Shanghai 200072, China;
2 Information Center, Criminal Investigation Department of Shanghai Public Security Bureau, Shanghai 200083, China;
3 Shanghai Engineering Research Center of Motion Picture Special Effects, Shanghai 200072, China;
4 Shanghai Film Academy, Shanghai University, Shanghai 200072, China

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摘要 本文立足于图像分类中的细粒度分类问题,提出了能实现车辆细分类的基于局部加强PCANET神经网络算法。该算法使用层级少、参数简单的PCANET无监督网络,实现了算法简单、人工标记简便、训练时间少的车型识别应用。经实验测试表明,PCANET方法较之传统法模式识别和多层级CNN神经网络,在样本库量级变化、角度偏移、训练速度各方面测试中获得了最佳平衡;正确局部特征的选取、加入与整体特征不同尺度的局部特征对识别率上升起到了很重要的加成作用;而7(12角度)角度样本建模方案,被证明是解决实践中角度偏移导致识别率下降问题行之有效的方案。
关键词细粒度分类   PCANET   局部加强   车型识别     
Abstract: In this paper, we propose a locally enhanced PCANet neural network for fine-grained classification of vehicles. The proposed method adopts the PCANet unsupervised network with a smaller number of layers and simple parameters compared with the majority of state-of-the-art machine learning methods. It simplifies calculation steps and manual labeling, and enables vehicle types to be recognized without time-consuming training. Experimental results show that compared with the traditional pattern recognition methods and the multi-layer CNN methods, the proposed method achieves optimal balance in terms of varying scales of sample libraries, angle deviations, and training speed. It also indicates that introducing appropriate local features that have different scales from the general feature is very instrumental in improving recognition rate. The 7-angle in 180° (12-angle in 360°) classification modeling scheme is proven to be an effective approach, which can solve the problem of suffering decrease in recognition rate due to angle deviations, and add the recognition accuracy in practice.
Keywordsfine-grained classification   PCANet   local enhancement   vehicle type recognition     
Received 2016-10-11;
About author: Qian Wang is a senior engineer in Information Center of Criminal Investigation Department of Shanghai Public Security Bureau, Shanghai, and also a Ph.D. candidate of School of Computer Engineering and Science, Shanghai University, Shanghai. Her research interests mainly include intelligent surveillance analysis, deep learning neural network, and intelligent human biological characteristic identification
引用本文:   
Qian Wang, You-Dong Ding.基于局部加强PCAnet神经网络的细粒度车型识别方法[J]  Journal of Computer Science and Technology , 2018,V33(2): 335-350
Qian Wang, You-Dong Ding.A Novel Fine-Grained Method for Vehicle Type Recognition Based on the Locally Enhanced PCANet Neural Network[J]  Journal of Computer Science and Technology, 2018,V33(2): 335-350
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