TY - GEN
T1 - DeepSSH
T2 - 25th IEEE International Conference on Image Processing, ICIP 2018
AU - Zhao, Ya
AU - Luo, Sihui
AU - Yang, Yezhou
AU - Song, Mingli
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/29
Y1 - 2018/8/29
N2 - For large collections of gallery images captured by sparsely distributed cameras, we often employ hashing based approaches to enhance the efficiency of person re-identification (re-id). However, these hashing based approaches fail to provide semantically explainable encoding in solving the re-id problem, which makes it infeasible to identify the correct matches in the collection by just using a semantic query. To overcome this limitation, we propose a new deep hashing network called Deep Semantic Structured Hashing (DeepSSH) to obtain the semantic structured representation of human. In the proposed DeepSSH framework, both the mid-level human attributes and the high-level ID labels are used to learn a deep hashing network. Then, based on the obtained semantic structured hash code and the attribute labels, we learn a decoder to find the partial hash code corresponding to the specified attributes. Finally, a new grain scalable re-id framework is constructed to support semantic query of a person by providing partial or full semantic description of a person instead of the whole photo. Experimental results show that DeepSSH is comparable with state-of-the-art hashing-based person re-id approaches, and the experiment in semantic analysis shows that our hash code owns semantic meaning indeed.
AB - For large collections of gallery images captured by sparsely distributed cameras, we often employ hashing based approaches to enhance the efficiency of person re-identification (re-id). However, these hashing based approaches fail to provide semantically explainable encoding in solving the re-id problem, which makes it infeasible to identify the correct matches in the collection by just using a semantic query. To overcome this limitation, we propose a new deep hashing network called Deep Semantic Structured Hashing (DeepSSH) to obtain the semantic structured representation of human. In the proposed DeepSSH framework, both the mid-level human attributes and the high-level ID labels are used to learn a deep hashing network. Then, based on the obtained semantic structured hash code and the attribute labels, we learn a decoder to find the partial hash code corresponding to the specified attributes. Finally, a new grain scalable re-id framework is constructed to support semantic query of a person by providing partial or full semantic description of a person instead of the whole photo. Experimental results show that DeepSSH is comparable with state-of-the-art hashing-based person re-id approaches, and the experiment in semantic analysis shows that our hash code owns semantic meaning indeed.
KW - CNN
KW - Hashing
KW - Person re-identification
UR - http://www.scopus.com/inward/record.url?scp=85062910775&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062910775&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2018.8451107
DO - 10.1109/ICIP.2018.8451107
M3 - Conference contribution
AN - SCOPUS:85062910775
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1653
EP - 1657
BT - 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PB - IEEE Computer Society
Y2 - 7 October 2018 through 10 October 2018
ER -