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231225s2018 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2018.2839522
|2 doi
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|a eng
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| 100 |
1 |
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|a Huang, Chang-Qin
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Object-Location-Aware Hashing for Multi-Label Image Retrieval via Automatic Mask Learning
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| 264 |
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|c 2018
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| 336 |
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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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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| 500 |
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Learning-based hashing is a leading approach of approximate nearest neighbor search for large-scale image retrieval. In this paper, we develop a deep supervised hashing method for multi-label image retrieval, in which we propose to learn a binary "mask" map that can identify the approximate locations of objects in an image, so that we use this binary "mask" map to obtain length-limited hash codes which mainly focus on an image's objects but ignore the background. The proposed deep architecture consists of four parts: 1) a convolutional sub-network to generate effective image features; 2) a binary "mask" sub-network to identify image objects' approximate locations; 3) a weighted average pooling operation based on the binary "mask" to obtain feature representations and hash codes that pay most attention to foreground objects but ignore the background; and 4) the combination of a triplet ranking loss designed to preserve relative similarities among images and a cross entropy loss defined on image labels. We conduct comprehensive evaluations on four multi-label image data sets. The results indicate that the proposed hashing method achieves superior performance gains over the state-of-the-art supervised or unsupervised hashing baselines
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| 650 |
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4 |
|a Journal Article
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| 700 |
1 |
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|a Yang, Shang-Ming
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Pan, Yan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lai, Han-Jiang
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 27(2018), 9 vom: 21. Sept., Seite 4490-4502
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|g volume:27
|g year:2018
|g number:9
|g day:21
|g month:09
|g pages:4490-4502
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|u http://dx.doi.org/10.1109/TIP.2018.2839522
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