https://scholars.lib.ntu.edu.tw/handle/123456789/427476
標題: | Enhanced Canonical Correlation Analysis with Local Density for Cross-Domain Visual Classification | 作者: | W.-J. Ko J.-Y.- Yu W.-Y. Chen Y.-C. F. Wang YU-CHIANG WANG 王鈺強 |
關鍵字: | Canonical Correlation Analysis; Cross-View Action Recognition; Person Re-identification | 公開日期: | 2017 | 來源出版物: | IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) | 摘要: | Real-world visual classification tasks typically need to deal with data observed from different domains. Inspired by canonical correlation analysis (CCA), we propose an enhanced CCA with local density for associating and recognizing cross-domain data. In addition to maximizing the correlation of the projected cross-domain data, our CCA model further exploits the local density information observed from each domain. As a result, our CCA not only exhibits excellent abilities in identifying representative data, noisy data like outliers can be further suppressed during the derivation of our CCA subspace. In our experiments, we successfully apply the proposed methods for solving two cross-domain classification tasks: person re-identification and cross-view action recognition. © 2017 IEEE. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/427476 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85023753627&doi=10.1109%2fICASSP.2017.7952458&partnerID=40&md5=2df3e8174e034a59962de268fc431b7b |
ISSN: | 15206149 | DOI: | 10.1109/icassp.2017.7952458 |
顯示於: | 電信工程學研究所 |
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