正则化独立测度矩阵的行人再识别*

针对当前基于距离测度学习的行人再识别算法中因训练样本少而出现的过拟合问题,提出正则化独立测度矩阵的行人再识别算法.该算法首先在4个不同的颜色空间单独学习测度矩阵,然后分别对相应的测度矩阵进行正则化,测试样本通过正则化后的测度矩阵进行相似性度量,最后结合相似性度量结果得到最终相似度.实验表明,相比原有算法,文中算法在性能上有进一步提升,并可改善训练样本少时出现的过拟合问题.

Person Re-identification Based on Regularization of Independent Measure Matrix

To solve the over-fitting problem caused by less training samples in the current person re-identification method based on distance metric learning, a person re-identification algorithm based on regularization of independent measure matrix is proposed. Firstly, the features extracted from four different color spaces are used to learn four different measure matrices. Then, the corresponding matrixes are regularized respectively, and the similarity of testing examples is measured by the regularized matrices. Finally, the final similarity is obtained by fusing results of the similarity measure. Experimental results show the improvement of the proposed method in performance for the over-fitting problem caused by less training samples.