计算机工程与应用2020，Vol.56Issue(1)：136-141,6.DOI:10.3778/j.issn.1002-8331.1809-0101
基于改进稀疏栈式编码的车型识别
Vehicle Identification Based on Improved Sparse Stack Coding
摘要
Abstract
In order to improve the accuracy of sparse stack coding for vehicle type identification, this paper proposes a vehicle identification method based on improved sparse stack coding. The layer-by-layer unsupervised method is used to train the network structure, and the feature dictionary is learned from a large number of unmarked data sets. Then, the convolution and pooling modules are introduced on the basis of sparse stack coding, and the learned feature dictionary is taken as a convolution kernel. Feature map of the image is obtained by convolving and pooling the image containing the vehicle. Finally, supervised fine-tuning is performed on a small number of tag data sets by using the softmax classifier. By experimenting on the BIT-Vehicle dataset, the improved algorithm is superior to the traditional sparse stacking algorithm. In the dataset with less labeling, the recognition accuracy is better than the neural network algorithm.关键词
车型识别/稀疏栈式编码/卷积/池化/特征字典Key words
vehicle type classification/sparse stack coding/convolution/pooling/feature dictionary分类
信息技术与安全科学引用本文复制引用
代乾龙,孙伟..基于改进稀疏栈式编码的车型识别[J].计算机工程与应用,2020,56(1):136-141,6.基金项目
国家自然科学基金(No.61403394). （No.61403394）
