Bagging–boosting-based semi-supervised multi-hashing with query-adaptive re-ranking

Autor: Wing W. Y. Ng, Xizhao Wang, Daniel S. Yeung, Xing Tian, Xiancheng Zhou
Rok vydání: 2018
Předmět:
Zdroj: Neurocomputing. 275:916-923
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2017.09.042
Popis: 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.
Databáze: OpenAIRE