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- W2891980168 abstract "At present, hashing algorithm has been combined with deep learning to accelerate image retrieval. Against this background, there are many ways to construct hashing, but most of the methods do not show excellent performance in reducing semantic loss. At the same time, the vast majority of cases that adopt hashing algorithm and obtain successful cases involve the identification model requiring labels. So we propose a high precision with the combination of self-learning hash algorithm (HPSLH) to conduct experiments, the algorithm can not only through the analysis of the data itself, and construct a set of false label, then using the data from the identification model of deep learning can also avoid enormous semantic loss in the process of our hash. Through experiments on traditional datasets, this method can achieve the desired goal." @default.
- W2891980168 created "2018-09-27" @default.
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- W2891980168 date "2018-01-01" @default.
- W2891980168 modified "2023-09-28" @default.
- W2891980168 title "High Precision Self-learning Hashing for Image Retrieval" @default.
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- W2891980168 doi "https://doi.org/10.1007/978-981-13-2203-7_57" @default.
- W2891980168 hasPublicationYear "2018" @default.
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