【论文题目】Person re-identification via integrating patch-based metric learning and local salience learning
【作 者】Zhicheng Zhao, Binlin Zhao, Fei Su 点击下载PDF全文
【关 键 字】Person re-identification, CNN feature, Patch-based metric learning, Local salience learning, Cross-dataset
In this paper, aiming at improving the generalization capability, we propose a cross-dataset person re- identification framework via integrating patch-based metric learning and local salience learning. Firstly, Convolution Neural Network(CNN) features are extracted to represent patches of a person. Secondly, only two positive patch-pairs are chosen and input into a Large Margin Nearest Neighbour(LMNN) network to learn two patch-based metric matrices for feature projection respectively. Thirdly, according to projected new features, a local salience learning algorithm based on Kmeans clustering is proposed to train the weights of patches. Finally, the similarity of image-pair is computed by a weighted summing of all patches. The experimental results indicate that the proposed method outperforms existing conventional approaches based on hand-crafted features and achieves a comparable performance with most recent CNN-based methods, which demonstrates our method’s effectiveness and practicality. It does not need a large-scale labeled training dataset, and has a high matching rate with a low computation complexity.
【发 表 年】2017
【发 表 月】3