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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2020.3042193
|2 doi
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Lin, Mingbao
|e verfasserin
|4 aut
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| 245 |
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|a Fast Class-Wise Updating for Online Hashing
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 04.04.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Online image hashing has received increasing research attention recently, which processes large-scale data in a streaming fashion to update the hash functions on-the-fly. To this end, most existing works exploit this problem under a supervised setting, i.e., using class labels to boost the hashing performance, which suffers from the defects in both adaptivity and efficiency: First, large amounts of training batches are required to learn up-to-date hash functions, which leads to poor online adaptivity. Second, the training is time-consuming, which contradicts with the core need of online learning. In this paper, a novel supervised online hashing scheme, termed Fast Class-wise Updating for Online Hashing (FCOH), is proposed to address the above two challenges by introducing a novel and efficient inner product operation. To achieve fast online adaptivity, a class-wise updating method is developed to decompose the binary code learning and alternatively renew the hash functions in a class-wise fashion, which well addresses the burden on large amounts of training batches. Quantitatively, such a decomposition further leads to at least 75 percent storage saving. To further achieve online efficiency, we propose a semi-relaxation optimization, which accelerates the online training by treating different binary constraints independently. Without additional constraints and variables, the time complexity is significantly reduced. Such a scheme is also quantitatively shown to well preserve past information during updating hashing functions. We have quantitatively demonstrated that the collective effort of class-wise updating and semi-relaxation optimization provides a superior performance comparing to various state-of-the-art methods, which is verified through extensive experiments on three widely-used datasets
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|a Journal Article
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| 700 |
1 |
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|a Ji, Rongrong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sun, Xiaoshuai
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Baochang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Huang, Feiyue
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tian, Yonghong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tao, Dacheng
|e verfasserin
|4 aut
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| 773 |
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8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 5 vom: 03. Mai, Seite 2453-2467
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
8 |
|g volume:44
|g year:2022
|g number:5
|g day:03
|g month:05
|g pages:2453-2467
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2020.3042193
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