基于指数权重局部聚合向量特征的轮毂型号识别

Wheel Model Identification Based on Index Weight Vector of Aggragate Locally Descriptor Features

在轮毂型号识别过程中,为了能在大量轮型库中快速识别正确的轮型,提出了基于指数权重局部聚合向量(VLAD)特征的轮型识别方法。VLAD特征是针对BOW特征的改进版,用待分类特征和聚类中心的累积残差代替特征的累加数目,采用四近邻软分配的查找方式,相对于一对一的分配规则具有更好的鲁棒性。最后把得到的VLAD向量进行主成分分析降维,并在降维VLAD的基础上将指数权重和VLAD向量的各数据相乘以削减个别不稳定值,最后通过特征向量的对比来找到最相似图片,识别过程具有非接触、灵活、准确的优点,实验表明在提高图片识别率的同时也具有较好的鲁棒性。

In the process of wheel model identification,in order to quickly identify the correct wheel model in a large number of wheel model data base,a identification algorithm based on index weight VLAD(vector of aggragate locally descriptor) is proposed.The VLAD feature is an improved version of the BOW,using the cumulative residuals between features and cluster centers replace the cumulative number of features,as the searching algorithm we use four nearest neighbor soft allocation,and has better robustness than the one to one allocation rule.Then the PCA is used to reduce dimensions of VLAD obtained above,the multiply index weight and the data of descending dimension VLAD to reduce individual instability value,and finally,the most similar pictures are found by comparing the feature vectors.The experimental results shows that this algorithm has the advantages of non-contact,perform well on flexibility and accuracy,it also improves the identification rate and has good robustness.