计算机与数字工程2018，Vol.46Issue(5)：861-865,895,6.DOI:10.3969/j.issn.1672-9722.2018.05.003
基于改进词包模型的车型识别算法
Vehicle Type Recognition Algorithm Based on Improved Bag of Words Model
摘要
Abstract
Aiming at the problem that vehicle type recognition algorithms based on original bag-of-words model run slowly and inaccurately,a vehicle type recognition algorithm based on improved bag-of-words model is proposed. Firstly,Dense-Surf method is used to extract features when stratage of dense sample has being optimized to speed up the process;Secondly,a new algo-rithm named feature context-vector quantization(FC-VQ)is proposed to encode features,from which the location information of features can be expressed clearly,and the recognition rate increases synchronously. Finally,fast histogram intersection kernel is used as kernel function of SVM classifier for training and recognizing processes of encoded features.Experimental results show that the algorithm proposed in this paper has higher recognition rate and faster recognition speed compared with other vehicle type recog-nition algorithms.关键词
车型识别/词包模型/Dense-SURF/FC-VQ编码/快速直方图相交核Key words
vehicle type recognition/bag-of-words model/Dense-SURF/FC-VQ encoding/fast histogram intersection kernel分类
信息技术与安全科学引用本文复制引用
康珮珮,于凤芹,陈莹..基于改进词包模型的车型识别算法[J].计算机与数字工程,2018,46(5):861-865,895,6.基金项目
国家自然科学基金项目(编号:61573168)资助. （编号:61573168）
