A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/1901.01474v1.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
Filters
Bilinear Supervised Hashing Based on 2D Image Features
[article]
<span title="2019-01-05">2019</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
Preserved Fulltext
Most of the existing hashing methods focus on learning the low-dimensional vectorized binary features based on the high-dimensional raw vectorized features. ...
However, studies on how to obtain preferable binary codes from the original 2D image features for retrieval is very limited. ...
More importantly, the SDH method is based on vectorized features instead of 2D features. ...
<span class="external-identifiers">
<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.01474v1">arXiv:1901.01474v1</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nkwqkqev3nh5hoyorqnejeor44">fatcat:nkwqkqev3nh5hoyorqnejeor44</a>
</span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200928110434/https://arxiv.org/pdf/1901.01474v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/0a/3e/0a3e9b00aa5373414958bc4b4a743d4aeecb882f.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.01474v1" title="arxiv.org access">
<button class="ui compact blue labeled icon button serp-button">
<i class="file alternate outline icon"></i>
arxiv.org
</button>
</a>
Deep Sketch-Shape Hashing With Segmented 3D Stochastic Viewing
<span title="">2019</span>
<i title="IEEE">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ilwxppn4d5hizekyd3ndvy2mii" style="color: black;">2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)</a>
</i>
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit <a rel="external noopener" href="http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Deep_Sketch-Shape_Hashing_With_Segmented_3D_Stochastic_Viewing_CVPR_2019_paper.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
Sketch-based 3D shape retrieval has been extensively studied in recent works, most of which focus on improving the retrieval accuracy, whilst neglecting the efficiency. ...
In this paper, we propose a novel framework for efficient sketch-based 3D shape retrieval, i.e., Deep Sketch-Shape Hashing (DSSH), which tackles the challenging problem from two perspectives. ...
This indicates that DSSH can learn discriminative features based on L D . 2) L C . ...
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvpr.2019.00088">doi:10.1109/cvpr.2019.00088</a>
<a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cvpr/ChenQ00S0019.html">dblp:conf/cvpr/ChenQ00S0019</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uwgxwcbfafdjrg4uxzw27euc3m">fatcat:uwgxwcbfafdjrg4uxzw27euc3m</a>
</span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200318210919/http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Deep_Sketch-Shape_Hashing_With_Segmented_3D_Stochastic_Viewing_CVPR_2019_paper.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/4e/c5/4ec5fc8daf92dec2b086a98e34429f99ec716c94.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvpr.2019.00088">
<button class="ui left aligned compact blue labeled icon button serp-button">
<i class="external alternate icon"></i>
ieee.com
</button>
</a>
A novel facial image recognition method based on perceptual hash using quintet triple binary pattern
<span title="2020-08-12">2020</span>
<i title="Springer Science and Business Media LLC">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7inqmh346zfjjizjyieh7ijtma" style="color: black;">Multimedia tools and applications</a>
</i>
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit <a rel="external noopener" href="https://link.springer.com/content/pdf/10.1007/s11042-020-09439-8.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
In this study, a novel face recognition method based on perceptual hash is presented. The proposed perceptual hash is utilized for preprocessing and feature extraction phases. ...
Discrete Wavelet Transform (DWT) and a novel graph based binary pattern, called quintet triple binary pattern (QTBP), are used. ...
[51] suggested a method based on LBP and ensemble learning for face recognition. ...
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11042-020-09439-8">doi:10.1007/s11042-020-09439-8</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mwfxktgefvgndofq7lou3flgom">fatcat:mwfxktgefvgndofq7lou3flgom</a>
</span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201108145714/https://link.springer.com/content/pdf/10.1007/s11042-020-09439-8.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/38/82/388245fc02554c215805a77a1d0c7e553995d9a2.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11042-020-09439-8">
<button class="ui left aligned compact blue labeled icon button serp-button">
<i class="unlock alternate icon" style="background-color: #fb971f;"></i>
springer.com
</button>
</a>
Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing Images
[article]
<span title="2019-04-02">2019</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/1904.01258v1.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
Other Versions
<span title="2021-01-06">2021</span>
<span class="release-stage">accepted</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.01258v3">arXiv:1904.01258v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/w3vumrrlybdqneqz7wfi2lkg4i">fatcat:w3vumrrlybdqneqz7wfi2lkg4i</a> </span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.01258v3">arXiv:1904.01258v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/w3vumrrlybdqneqz7wfi2lkg4i">fatcat:w3vumrrlybdqneqz7wfi2lkg4i</a> </span>
<span title="2020-02-15">2020</span>
<span class="release-stage">accepted</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.01258v2">arXiv:1904.01258v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qaax7lhsg5e5vgkrv6hz7kff6q">fatcat:qaax7lhsg5e5vgkrv6hz7kff6q</a> </span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.01258v2">arXiv:1904.01258v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qaax7lhsg5e5vgkrv6hz7kff6q">fatcat:qaax7lhsg5e5vgkrv6hz7kff6q</a> </span>
To overcome this problem, in this paper we introduce a metric-learning based hashing network, which learns: 1) a semantic-based metric space for effective feature representation; and 2) compact binary ...
The traditional hashing methods in RS usually exploit hand-crafted features to learn hash functions to obtain binary codes, which can be insufficient to optimally represent the information content of RS ...
We also report the ablation studies conducted on the AID archive. ...
<span class="external-identifiers">
<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.01258v1">arXiv:1904.01258v1</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/maqszcawmzfppmv74vohhe5eiu">fatcat:maqszcawmzfppmv74vohhe5eiu</a>
</span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200823001908/https://arxiv.org/pdf/1904.01258v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/7f/47/7f474b3c900cfbba649e30dd158b5a5e4f458392.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.01258v1" title="arxiv.org access">
<button class="ui compact blue labeled icon button serp-button">
<i class="file alternate outline icon"></i>
arxiv.org
</button>
</a>
TransHash: Transformer-based Hamming Hashing for Efficient Image Retrieval
[article]
<span title="2021-05-05">2021</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/2105.01823v1.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
To learn fine-grained features, we innovate a dual-stream feature learning on top of the transformer to learn discriminative global and local features. (2) Besides, we adopt a Bayesian learning scheme ...
We perform comprehensive experiments on three widely-studied datasets: CIFAR-10, NUSWIDE and IMAGENET. ...
Based on the way they extract features, existing hashing-based works can be divided into two categories, namely, shallow methods and deep learning-based methods. ...
<span class="external-identifiers">
<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.01823v1">arXiv:2105.01823v1</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h7fzcwnit5bc7e23mrssf6dp5e">fatcat:h7fzcwnit5bc7e23mrssf6dp5e</a>
</span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210507024550/https://arxiv.org/pdf/2105.01823v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/03/10/0310f2e82ccb474580512cfd17dfc0fb102d1f06.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.01823v1" title="arxiv.org access">
<button class="ui compact blue labeled icon button serp-button">
<i class="file alternate outline icon"></i>
arxiv.org
</button>
</a>
Learning to Rank 3D Features
[chapter]
<span title="">2014</span>
<i title="Springer International Publishing">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a>
</i>
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit <a rel="external noopener" href="http://www.merl.com/publications/docs/TR2014-078.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
We propose a max-margin learning framework to identify discriminative features on the surface of three dimensional objects. ...
We propose a max-margin learning framework to identify discriminative features on the surface of three dimensional objects. ...
The weighting scheme based on hash table bins has connection to well-known bag-of-words model, where hash keys can be considered the codewords mapping features to a histogram. ...
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-10590-1_34">doi:10.1007/978-3-319-10590-1_34</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lhneyqh27fhjzcd6vmtvllldjq">fatcat:lhneyqh27fhjzcd6vmtvllldjq</a>
</span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20160812133555/http://www.merl.com/publications/docs/TR2014-078.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/b3/1c/b31cc80e33625a320e1f249f2aebc3a72dfadf1b.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-10590-1_34">
<button class="ui left aligned compact blue labeled icon button serp-button">
<i class="external alternate icon"></i>
springer.com
</button>
</a>
Online Learning of Binary Feature Indexing for Real-Time SLAM Relocalization
[chapter]
<span title="">2015</span>
<i title="Springer International Publishing">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a>
</i>
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit <a rel="external noopener" href="http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/ACCV_2014/pages/workshop2/pdffiles/workshop2-paper5-Online%20Learning%20of%20Binary%20Feature%20Indexing%20for%20Real-time%20SLAM%20Relocalization.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
In the learning process, the hash keys are constructed with the aim of obtaining uniform hash buckets and high collision rates, which makes the method more efficient on approximate nearest neighbor search ...
Being different from the popular Locality Sensitive Hashing (LSH), the proposed method construct the hash keys by an online learning process instead of pure randomness. ...
[10] have designed a relocalization module based on binary features [11] [12] [13] . ...
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-16628-5_15">doi:10.1007/978-3-319-16628-5_15</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fajumdby6vemtn64vpnymiddne">fatcat:fajumdby6vemtn64vpnymiddne</a>
</span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170812170750/http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/ACCV_2014/pages/workshop2/pdffiles/workshop2-paper5-Online%20Learning%20of%20Binary%20Feature%20Indexing%20for%20Real-time%20SLAM%20Relocalization.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/20/2a/202ab9e474ce9eda453b6fd728ce1ecde5b16bea.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-16628-5_15">
<button class="ui left aligned compact blue labeled icon button serp-button">
<i class="external alternate icon"></i>
springer.com
</button>
</a>
Deep Hash Remote Sensing Image Retrieval with Hard Probability Sampling
<span title="2020-08-27">2020</span>
<i title="MDPI AG">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a>
</i>
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit <a rel="external noopener" href="https://res.mdpi.com/d_attachment/remotesensing/remotesensing-12-02789/article_deploy/remotesensing-12-02789-v2.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
Therefore, a growing number of studies are focusing on remote sensing image retrieval. ...
Given the above considerations, we propose a deep hash remote sensing image retrieval method, called the hard probability sampling hash retrieval method (HPSH), which combines hash code learning with hard ...
X.S. conducted experimental verification of the deep hard probability sampling hash retrieval method, finished the paper and corrected it for final publication. ...
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/rs12172789">doi:10.3390/rs12172789</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gspneonmbjbcriomrbqpbvdp7q">fatcat:gspneonmbjbcriomrbqpbvdp7q</a>
</span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200829113807/https://res.mdpi.com/d_attachment/remotesensing/remotesensing-12-02789/article_deploy/remotesensing-12-02789-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/02/d3/02d35eb081a66475f80da2eb82ed0efd4f5f8e19.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/rs12172789">
<button class="ui left aligned compact blue labeled icon button serp-button">
<i class="unlock alternate icon" style="background-color: #fb971f;"></i>
mdpi.com
</button>
</a>
TransHash: Transformer-based Hamming Hashing for Efficient Image Retrieval
<span title="2022-06-27">2022</span>
<i title="ACM">
Proceedings of the 2022 International Conference on Multimedia Retrieval
</i>
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit <a rel="external noopener" href="https://dl.acm.org/doi/pdf/10.1145/3512527.3531405">the original URL</a>. The file type is <code>application/pdf</code>.
To learn fine-grained features, we innovate a dual-stream multi-granular feature learning on top of the transformer to learn discriminative global and local features. (2) Besides, we adopt a Bayesian learning ...
We perform comprehensive experiments on three widely-studied datasets: CIFAR-10, NUSWIDE and IMAGENET. ...
Based on the way they extract features, existing hashing-based works can be divided into two categories, namely, shallow methods and deep learning-based methods. ...
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3512527.3531405">doi:10.1145/3512527.3531405</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/j7fvd7v52veb3oh3j5svge6pcq">fatcat:j7fvd7v52veb3oh3j5svge6pcq</a>
</span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220628044201/https://dl.acm.org/doi/pdf/10.1145/3512527.3531405" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/4e/43/4e431e3ec36133416517cbd2c00942478704d74c.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3512527.3531405">
<button class="ui left aligned compact blue labeled icon button serp-button">
<i class="external alternate icon"></i>
acm.org
</button>
</a>
Fast Neuroimaging-Based Retrieval for Alzheimer's Disease Analysis
[chapter]
<span title="">2016</span>
<i title="Springer International Publishing">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a>
</i>
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit <a rel="external noopener" href="http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC5614455&blobtype=pdf">the original URL</a>. The file type is <code>application/pdf</code>.
This paper proposes a framework of fast neuroimaging-based retrieval and AD analysis, by three key steps: (1) landmark detection, which efficiently extracts landmark-based neuroimaging features without ...
landmarks; and (3) hashing, which converts high-dimensional features of subjects into binary codes, for efficiently conducting approximate nearest neighbor search and diagnosis of AD. ...
To this end, we propose a novel landmark selection method that integrates subspace learning and 2D feature selection in a unified framework. ...
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-47157-0_38">doi:10.1007/978-3-319-47157-0_38</a>
<a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28959800">pmid:28959800</a>
<a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5614455/">pmcid:PMC5614455</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/frhzoivdi5dxtajqqj5tznh66m">fatcat:frhzoivdi5dxtajqqj5tznh66m</a>
</span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200211144248/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC5614455&blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/21/44/21449648ae48aa0e49daaea7a6a1fd619f8fdb73.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-47157-0_38">
<button class="ui left aligned compact blue labeled icon button serp-button">
<i class="external alternate icon"></i>
springer.com
</button>
</a>
<a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5614455" title="pubmed link">
<button class="ui compact blue labeled icon button serp-button">
<i class="file alternate outline icon"></i>
pubmed.gov
</button>
</a>
Angular Deep Supervised Hashing for Image Retrieval
<span title="">2019</span>
<i title="Institute of Electrical and Electronics Engineers (IEEE)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a>
</i>
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit <a rel="external noopener" href="https://ieeexplore.ieee.org/ielx7/6287639/8600701/08825992.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
Deep learning based image hashing methods learn hash codes by using powerful feature extractors and nonlinear transformations to achieve highly efficient image retrieval. ...
In this paper, we propose a novel semantic learning based hashing method for image retrieval to optimize the deep features structure in the hash space from a perspective of angular view. ...
The angular based hashing approach learns the optimal hash code more directly because the learned features can be naturally converted to hash codes without quantization constraint in training process. ...
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2939650">doi:10.1109/access.2019.2939650</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/w2uszs7nzrcs3aym3dg23v6quy">fatcat:w2uszs7nzrcs3aym3dg23v6quy</a>
</span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210428224516/https://ieeexplore.ieee.org/ielx7/6287639/8600701/08825992.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/26/e4/26e45d5c2e4f5365a07b047737b1570708893703.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2939650">
<button class="ui left aligned compact blue labeled icon button serp-button">
<i class="unlock alternate icon" style="background-color: #fb971f;"></i>
ieee.com
</button>
</a>
Semi-supervised based Unknown Attack Detection in EDR Environment
<span title="2020-12-31">2020</span>
<i title="Korean Society for Internet Information (KSII)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hupfbobgkvepdnt5g32qxkypsy" style="color: black;">KSII Transactions on Internet and Information Systems</a>
</i>
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit <a rel="external noopener" href="http://itiis.org/digital-library/manuscript/file/24150/TIIS%20Vol%2014,%20No%2012-16.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
The proposed technology uses a combination of AutoEncoder and 1D CNN (1-Dimention Convolutional Neural Network) based on semi-supervised learning. ...
Endpoint Detection and Response (EDR) technology is focused on providing visibility, and strong countermeasures are lacking. ...
hashing pseudo-code
Fig. 8 . 8 Difference between 1D CNN and 2D CNN; (a) 1D CNN feature detector movement method; (b) 2D CNN feature detector movement method
Fig. 9 . 9 Cumulative distribution function ...
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3837/tiis.2020.12.016">doi:10.3837/tiis.2020.12.016</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/no6o5drfejarng7d6slm4egixi">fatcat:no6o5drfejarng7d6slm4egixi</a>
</span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210105025123/http://itiis.org/digital-library/manuscript/file/24150/TIIS%20Vol%2014,%20No%2012-16.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/ca/27/ca27195a9d42a4744d77e00d83008121ff9fe2e2.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3837/tiis.2020.12.016">
<button class="ui left aligned compact blue labeled icon button serp-button">
<i class="external alternate icon"></i>
Publisher / doi.org
</button>
</a>
Several Tunable GMM Kernels
[article]
<span title="2018-05-08">2018</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/1805.02830v1.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
In 2010, (Ping Li UAI'10) developed the method of "abc-robust-logitboost" and compared it with other supervised learning methods on datasets used by the deep learning literature. ...
In this study, we propose a series of "tunable GMM kernels" which are simple and perform largely comparably to tree methods on the same datasets. ...
, we will provide an empirical study on kernel SVMs based on the tunable GMM kernels. ...
<span class="external-identifiers">
<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1805.02830v1">arXiv:1805.02830v1</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/to3uoieqvzhhlfqhfcebvjaxoq">fatcat:to3uoieqvzhhlfqhfcebvjaxoq</a>
</span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200917165426/https://arxiv.org/pdf/1805.02830v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/11/a9/11a9c4e56c76d202da03af57656b46868b785edd.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1805.02830v1" title="arxiv.org access">
<button class="ui compact blue labeled icon button serp-button">
<i class="file alternate outline icon"></i>
arxiv.org
</button>
</a>
Stochastic Multiview Hashing for Large-Scale Near-Duplicate Video Retrieval
<span title="">2017</span>
<i title="Institute of Electrical and Electronics Engineers (IEEE)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sbzicoknnzc3tjljn7ifvwpooi" style="color: black;">IEEE transactions on multimedia</a>
</i>
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit <a rel="external noopener" href="https://www.research.manchester.ac.uk/portal/files/46115431/Manuscript_final_with_bios.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
Index Terms-Near-duplicate video retrieval, hashing, multiview learning, semi-supervised learning, divergence. ...
Reliable mapping functions, which convert multiple types of keyframe features, enhanced by auxiliary information such as video-keyframe association and ground truth relevance to binary hash code strings ...
ACKNOWLEDGEMENTS This work was supported by "The International Research Base for Developing Innovative Gerontechnology" sponsored by the national "111" project (No. B14025) of China. ...
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tmm.2016.2610324">doi:10.1109/tmm.2016.2610324</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/w3k32jluljbt7lvk7rax6tq6w4">fatcat:w3k32jluljbt7lvk7rax6tq6w4</a>
</span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190429084645/https://www.research.manchester.ac.uk/portal/files/46115431/Manuscript_final_with_bios.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/22/9d/229d1ee54d67db59b000bfa8ad1fcc1f2281788f.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tmm.2016.2610324">
<button class="ui left aligned compact blue labeled icon button serp-button">
<i class="external alternate icon"></i>
ieee.com
</button>
</a>
A Novel Unsupervised Convolutional Network Based on Gabor and (2D)2PCA for Feature Extraction and Recognition
<span title="">2016</span>
<i title="ICIC International">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wdc7itdnm5ekhoql3paw7mmr4q" style="color: black;">ICIC Express Letters</a>
</i>
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit <a rel="external noopener" href="http://www.icicel.org/ell/contents/2016/10/el-10-10-24.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
It is based on a convolutional structure and can extract more useful multi-level features. ...
However, features extracted by (2D) 2 PCA essentially are low-level and sensitive to distortions. To solve this problem, a new unsupervised deep learning network is proposed in this paper. ...
Compared with PCA, (2D) 2 PCA can learn more features. Furthermore, it is more efficient in computation. The reasons depend on two aspects. ...
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24507/icicel.10.10.2459">doi:10.24507/icicel.10.10.2459</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mpopugj5wven7fxpw6g4p4yyl4">fatcat:mpopugj5wven7fxpw6g4p4yyl4</a>
</span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220310020658/http://www.icicel.org/ell/contents/2016/10/el-10-10-24.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/73/18/73182d54dcff92daafde2542b7f4def8ae188f9d.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24507/icicel.10.10.2459">
<button class="ui left aligned compact blue labeled icon button serp-button">
<i class="external alternate icon"></i>
Publisher / doi.org
</button>
</a>
« Previous
Showing results 1 — 15 out of 6,053 results