Hu, W. and Fan, Y. and Xing, J. and Sun, L. and Cai, Z. and Maybank, Stephen J. (2018) Deep constrained siamese hash coding network and load-balanced locality-sensitive hashing for near duplicate image detection. IEEE Transactions on Image Processing 27 (9), pp. 4452-4464. ISSN 1057-7149.
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Abstract
We construct a new efficient near duplicate image detection method using a hierarchical hash code learning neural network and load-balanced Locality Sensitive Hashing (LSH) indexing. We propose a deep constrained siamese hash coding neural network combined with deep feature learning. Our neural network is able to extract effective features for near duplicate image detection. The extracted features are used to construct a LSH-based index. We propose a load-balanced LSH method to produce load-balanced buckets in the hashing process. The load-balanced LSH significantly reduces the query time. Based on the proposed load-balanced LSH, we design an effective and feasible algorithm for near duplicate image detection. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our deep siamese hash encoding network and load-balanced LSH.
Metadata
| Item Type: | Article |
|---|---|
| Additional Information: | (c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
| Keyword(s) / Subject(s): | Near duplicate image detection, Load-balanced locality-sensitive hashing, Deep constrained siamese neural network, Deep feature extraction |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Stephen Maybank |
| Date Deposited: | 31 May 2018 08:11 |
| Last Modified: | 09 Aug 2023 12:44 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/22505 |
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