Unsupervised Salience Learning for Person Re-identification
Refereed conference paper presented and published in conference proceedings


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摘要Human eyes can recognize person identities based on some small salient regions. However, such valuable salient information is often hidden when computing similarities of images with existing approaches. Moreover, many existing approaches learn discriminative features and handle drastic viewpoint change in a supervised way and require labeling new training data for a different pair of camera views. In this paper, we propose a novel perspective for person re-identification based on unsupervised salience learning. Distinctive features are extracted without requiring identity labels in the training procedure. First, we apply adjacency constrained patch matching to build dense correspondence between image pairs, which shows effectiveness in handling misalignment caused by large viewpoint and pose variations. Second, we learn human salience in an unsupervised manner. To improve the performance of person re-identification, human salience is incorporated in patch matching to find reliable and discriminative matched patches. The effectiveness of our approach is validated on the widely used VIPeR dataset and ETHZ dataset.
著者Zhao R, Ouyang WL, Wang XG
會議名稱26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
會議開始日23.06.2013
會議完結日28.06.2013
會議地點Portland
會議國家/地區美國
詳細描述organized by IEEE Computer Society,
出版年份2013
月份1
日期1
出版社IEEE
頁次3586 - 3593
電子國際標準書號978-0-7695-4989-7
國際標準期刊號1063-6919
語言英式英語
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence

上次更新時間 2021-24-02 於 00:04