کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6864956 | 1439552 | 2018 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Bagging-boosting-based semi-supervised multi-hashing with query-adaptive re-ranking
ترجمه فارسی عنوان
چند هشبه نیمه نظارت مبتنی بر بسته بندی با رتبه بندی رتبه بندی سازگار با پرس و جو
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کلمات کلیدی
بازیابی اطلاعات نیمه نظارت، چند هش بسته بندی تقویت،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 275, 31 January 2018, Pages 916-923
Journal: Neurocomputing - Volume 275, 31 January 2018, Pages 916-923
نویسندگان
Wing W.Y. Ng, Xiancheng Zhou, Xing Tian, Xizhao Wang, Daniel S. Yeung,