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 |
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Rok vydání: | 2018 |
Předmět: |
Universal hashing
Computer science business.industry Cognitive Neuroscience Dynamic perfect hashing Hash function Pattern recognition 2-choice hashing computer.software_genre Hash table Computer Science Applications K-independent hashing Locality-sensitive hashing Hopscotch hashing Hash tree Open addressing Artificial Intelligence Feature hashing Artificial intelligence Data mining business Extendible hashing computer Double hashing |
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 |
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