基于判别低秩矩阵恢复和稀疏表示的车标识别

Vehicle Logo Recognition Based on Discriminative Low-rank Matrix Recovery and Sparse Representation

针对现有的车标识别方法无法较好地处理阴影、遮挡、污损等情况下识别率低的问题,提出了基于判别低秩矩阵恢复和稀疏表示的车标识别方法。文中采用判别低秩矩阵恢复来纠正效果较差的训练样本,并通过学习一个低秩投影矩阵,将待测样本特征矩阵投影到相应低秩子空间来恢复干净的测试样本。并采用稀疏表示方式进行分类识别。同时,在Medialab LPR Database数据集上进行了对比实验,实验结果表明,该识别方法的性能要优于当前其他识别方法。

In consideration of the problem that the existing vehicle logo recognition methods cannot handle the vehicle logo recognition under unsatisfactory situations,such as shadows,occlusions,stains,which cause low recognition rate. Therefore,an algorithm based on discriminative low-rank matrix recovery and sparse representation is proposed. First,discriminative low-rank matrix recovery is used to correct the unsatisfactory training samples,and then it learns a low-rank projection matrix to correct the corrupted testing sample by projecting the sample onto its corresponding underlying subspaces. Finally,the sparse representation method is used to classify the testing sample.Comparative experiments made on Medialab LPR Dataset show that the performance of the method is better than other vehicle logo recognition methods.