Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6939635 | Pattern Recognition | 2018 | 32 Pages |
Abstract
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Zhicheng Zhao, Binlin Zhao, Fei Su,