PMID- 30106725
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20180907
LR  - 20180907
IS  - 1941-0042 (Electronic)
IS  - 1057-7149 (Linking)
VI  - 27
IP  - 12
DP  - 2018 Dec
TI  - Deep Discrete Supervised Hashing.
PG  - 5996-6009
LID - 10.1109/TIP.2018.2864894 [doi]
AB  - Hashing has been widely used for large-scale search due to its low storage cost 
      and fast query speed. By using supervised information, supervised hashing can 
      significantly outperform unsupervised hashing. Recently, discrete supervised 
      hashing and feature learning based deep hashing are two representative progresses 
      in supervised hashing. On one hand, hashing is essentially a discrete 
      optimization problem. Hence, utilizing supervised information to directly guide 
      discrete (binary) coding procedure can avoid sub-optimal solution and improve the 
      accuracy. On the other hand, feature learning based deep hashing, which 
      integrates deep feature learning and hash-code learning into an end-to-end 
      architecture, can enhance the feedback between feature learning and hash-code 
      learning. The key in discrete supervised hashing is to adopt supervised 
      information to directly guide the discrete coding procedure in hashing. The key 
      in deep hashing is to adopt the supervised information to directly guide the deep 
      feature learning procedure. However, most deep supervised hashing methods cannot 
      use the supervised information to directly guide both discrete (binary) coding 
      procedure and deep feature learning procedure in the same framework. In this 
      paper, we propose a novel deep hashing method, called deep discrete supervised 
      hashing (DDSH). DDSH is the first deep hashing method which can utilize pairwise 
      supervised information to directly guide both discrete coding procedure and deep 
      feature learning procedure and thus enhance the feedback between these two 
      important procedures. Experiments on four real datasets show that DDSH can 
      outperform other state-of-the-art baselines, including both discrete hashing and 
      deep hashing baselines, for image retrieval.
FAU - Jiang, Qing-Yuan
AU  - Jiang QY
FAU - Cui, Xue
AU  - Cui X
FAU - Li, Wu-Jun
AU  - Li WJ
LA  - eng
PT  - Journal Article
DEP - 20180810
PL  - United States
TA  - IEEE Trans Image Process
JT  - IEEE transactions on image processing : a publication of the IEEE Signal 
      Processing Society
JID - 9886191
EDAT- 2018/08/15 06:00
MHDA- 2018/08/15 06:01
CRDT- 2018/08/15 06:00
PHST- 2018/08/15 06:00 [pubmed]
PHST- 2018/08/15 06:01 [medline]
PHST- 2018/08/15 06:00 [entrez]
AID - 10.1109/TIP.2018.2864894 [doi]
PST - ppublish
SO  - IEEE Trans Image Process. 2018 Dec;27(12):5996-6009. doi: 
      10.1109/TIP.2018.2864894. Epub 2018 Aug 10.