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231225s2017 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2017.2737329
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|a eng
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|a Yanbin Hao
|e verfasserin
|4 aut
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| 245 |
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|a Unsupervised t-Distributed Video Hashing and Its Deep Hashing Extension
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|c 2017
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|a Text
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|a ƒaComputermedien
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|a Date Completed 30.07.2018
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| 500 |
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|a Date Revised 30.07.2018
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a In this paper, a novel unsupervised hashing algorithm, referred to as t-USMVH, and its extension to unsupervised deep hashing, referred to as t-UDH, are proposed to support large-scale video-to-video retrieval. To improve robustness of the unsupervised learning, the t-USMVH combines multiple types of feature representations and effectively fuses them by examining a continuous relevance score based on a Gaussian estimation over pairwise distances, and also a discrete neighbor score based on the cardinality of reciprocal neighbors. To reduce sensitivity to scale changes for mapping objects that are far apart from each other, Student t-distribution is used to estimate the similarity between the relaxed hash code vectors for keyframes. This results in more accurate preservation of the desired unsupervised similarity structure in the hash code space. By adapting the corresponding optimization objective and constructing the hash mapping function via a deep neural network, we develop a robust unsupervised training strategy for a deep hashing network. The efficiency and effectiveness of the proposed methods are evaluated on two public video collections via comparisons against multiple classical and the state-of-the-art methods
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Tingting Mu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Goulermas, John Y
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jianguo Jiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Richang Hong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Meng Wang
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 26(2017), 11 vom: 10. Nov., Seite 5531-5544
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|g volume:26
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|g number:11
|g day:10
|g month:11
|g pages:5531-5544
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|u http://dx.doi.org/10.1109/TIP.2017.2737329
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