File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.24963/ijcai.2017/499
- Scopus: eid_2-s2.0-85031892948
- Find via
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Conference Paper: Locality-constrained deep supervised hashing for image retrieval
Title | Locality-constrained deep supervised hashing for image retrieval |
---|---|
Authors | |
Issue Date | 2017 |
Citation | IJCAI International Joint Conference on Artificial Intelligence, 2017, v. 0, p. 3567-3573 How to Cite? |
Abstract | Deep Convolutional Neural Network (DCNN) based deep hashing has shown its success for fast and accurate image retrieval, however directly minimizing the quantization error in deep hashing will change the distribution of DCNN features, and consequently change the similarity between the query and the retrieved images in hashing. In this paper, we propose a novel Locality-Constrained Deep Supervised Hashing. By simultaneously learning discriminative DCNN features and preserving the similarity between image pairs, the hash codes of our scheme preserves the distribution of DCNN features thus favors the accurate image retrieval. The contributions of this paper are two-fold: i) Our analysis shows that minimizing quantization error in deep hashing makes the features less discriminative which is not desirable for image retrieval; ii) We propose a Locality-Constrained Deep Supervised Hashing which preserves the similarity between image pairs in hashing. Extensive experiments on the CIFAR-10 and NUS-WIDE datasets show that our method significantly boosts the accuracy of image retrieval, especially on the CIFAR-10 dataset, the improvement is usually more than 6% in terms of the MAP measurement. Further, our method demonstrates 10 times faster than state-of-the-art methods in the training phase. |
Persistent Identifier | http://hdl.handle.net/10722/345092 |
ISSN | 2020 SCImago Journal Rankings: 0.649 |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhu, Hao | - |
dc.contributor.author | Gao, Shenghua | - |
dc.date.accessioned | 2024-08-15T09:25:10Z | - |
dc.date.available | 2024-08-15T09:25:10Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | IJCAI International Joint Conference on Artificial Intelligence, 2017, v. 0, p. 3567-3573 | - |
dc.identifier.issn | 1045-0823 | - |
dc.identifier.uri | http://hdl.handle.net/10722/345092 | - |
dc.description.abstract | Deep Convolutional Neural Network (DCNN) based deep hashing has shown its success for fast and accurate image retrieval, however directly minimizing the quantization error in deep hashing will change the distribution of DCNN features, and consequently change the similarity between the query and the retrieved images in hashing. In this paper, we propose a novel Locality-Constrained Deep Supervised Hashing. By simultaneously learning discriminative DCNN features and preserving the similarity between image pairs, the hash codes of our scheme preserves the distribution of DCNN features thus favors the accurate image retrieval. The contributions of this paper are two-fold: i) Our analysis shows that minimizing quantization error in deep hashing makes the features less discriminative which is not desirable for image retrieval; ii) We propose a Locality-Constrained Deep Supervised Hashing which preserves the similarity between image pairs in hashing. Extensive experiments on the CIFAR-10 and NUS-WIDE datasets show that our method significantly boosts the accuracy of image retrieval, especially on the CIFAR-10 dataset, the improvement is usually more than 6% in terms of the MAP measurement. Further, our method demonstrates 10 times faster than state-of-the-art methods in the training phase. | - |
dc.language | eng | - |
dc.relation.ispartof | IJCAI International Joint Conference on Artificial Intelligence | - |
dc.title | Locality-constrained deep supervised hashing for image retrieval | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.24963/ijcai.2017/499 | - |
dc.identifier.scopus | eid_2-s2.0-85031892948 | - |
dc.identifier.volume | 0 | - |
dc.identifier.spage | 3567 | - |
dc.identifier.epage | 3573 | - |