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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2020.2981879
|2 doi
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|a pubmed24n1308.xml
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|a (NLM)32217477
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|a DE-627
|b ger
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|e rakwb
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| 041 |
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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 |
1 |
0 |
|a Similarity-Preserving Linkage Hashing for Online Image Retrieval
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| 264 |
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|c 2020
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| 336 |
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|a Text
|b txt
|2 rdacontent
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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 Revised 27.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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| 520 |
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|a Online image hashing aims to update hash functions on-the-fly along with newly arriving data streams, which has found broad applications in computer vision and beyond. To this end, most existing methods update hash functions simply using discrete labels or pairwise similarity to explore intra-class relationships, which, however, often deteriorates search performance when facing a domain gap or semantic shift. One reason is that they ignore the particular semantic relationships among different classes, which should be taken into account in updating hash functions. Besides, the common characteristics between the label vectors (can be regarded as a sort of binary codes) and to-be-learned binary hash codes have left unexploited. In this paper, we present a novel online hashing method, termed Similarity Preserving Linkage Hashing (SPLH), which not only utilizes pairwise similarity to learn the intra-class relationships, but also fully exploits a latent linkage space to capture the inter-class relationships and the common characteristics between label vectors and to-be-learned hash codes. Specifically, SPLH first maps the independent discrete label vectors and binary hash codes into a linkage space, through which the relative semantic distance between data points can be assessed precisely. As a result, the pairwise similarities within the newly arriving data stream are exploited to learn the latent semantic space to benefit binary code learning. To learn the model parameters effectively, we further propose an alternating optimization algorithm. Extensive experiments conducted on three widely-used datasets demonstrate the superior performance of SPLH over several state-of-the-art online hashing methods
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| 650 |
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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 Chen, Shen
|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 Lin, Chia-Wen
|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 (2020) vom: 24. März
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
8 |
|g year:2020
|g day:24
|g month:03
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| 856 |
4 |
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|u http://dx.doi.org/10.1109/TIP.2020.2981879
|3 Volltext
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