Journal
NEUROCOMPUTING
Volume 275, Issue -, Pages 916-923Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2017.09.042
Keywords
Semi-supervised information retrieval; Multi-hashing; Bagging; Boosting
Categories
Funding
- National Natural Science Foundation of China [61272201, 61572201]
- Fundamental Research Funds for the Central Universities [2017ZD052]
Ask authors/readers for more resources
Hashing-based methods have been widely applied in large scale image retrieval problem due to its high efficiency. In real world applications, it is difficult to require all images in a large database being labeled while unsupervised methods waste information from labeled images. Therefore, semi-supervised hashing methods are proposed to use partially labeled database to train hash functions using both the semantic and the unsupervised information. Multi-hashing methods achieve better precision-recall in comparison to single hashing method. However, current boosting-based multi-hashing methods do not improve performance after a small number of hash tables are created. Therefore, a bagging-boosting-based semi-supervised multi-hashing with query-adaptive re-ranking (BBSHR) is proposed in this paper. In the proposed method, an individual hash table of multi-hashing is trained using the boosting-based BSPLH, such that each hash bit corrects errors made by previous bits. Moreover, we propose a new semi-supervised weighting scheme for the query-adaptive re-ranking. Experimental results show that the proposed method yields better precision and recall rates for given numbers of hash tables and bits. (C) 2017 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available