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2020 Index IEEE Transactions on Biometrics, Behavior, and Identity Science Vol. 2
2020
IEEE Transactions on Biometrics Behavior and Identity Science
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., +, TBIOM Jan. 2020 26-39
Image retrieval
Error-Corrected Margin-Based Deep Cross-Modal Hashing for Facial Image
Retrieval. ...
., +, TBIOM Jan. 2020 15-25 Error-Corrected Margin-Based Deep Cross-Modal Hashing for Facial Image Retrieval. ...
doi:10.1109/tbiom.2020.3028623
fatcat:3vyvmzuhpffbpjngto3syzr3zq
Perlustration on Image Processing under Free Hand Sketch Based Image Retrieval
2018
EAI Endorsed Transactions on Internet of Things
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, neural networks, Fuzzy Logic and deep learning concept. ...
Image Retrieval to provide the results in a better way by adapting the approaches like Text Based Image Retrieval(TBIR) and Sketch Based Image Retrieval(SBIR). ...
[1] , has projected Deep Sketch Hashing (DSH), technique which uses three convolutional neural networks [13] (CNN) to encode normal images, free-hand sketches, and the sketch-tokens which act as a ...
doi:10.4108/eai.21-12-2018.159334
fatcat:2wjongwrhrfflm2amb3zyd52b4
Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion
[article]
2020
arXiv
pre-print
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Recently, deep neural networks have exhibited as a powerful architecture to well capture the nonlinear distribution of high-dimensional multimedia data, so naturally does for multi-modal data. ...
Throughout this survey, we further indicate that the critical components for this field go to collaboration, adversarial competition and fusion over multi-modal spaces. ...
Deep Multi-Modal Face Representation (MM-DFR) [23] first adopted multiple Convolutional Neural Networks (CNN) to extract features for face images. ...
arXiv:2006.08159v1
fatcat:g4467zmutndglmy35n3eyfwxku
Fast Training of Triplet-based Deep Binary Embedding Networks
[article]
2016
arXiv
pre-print
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In the second stage we propose to map the original image to compact binary codes via carefully designed deep convolutional neural networks (CNNs) and the hashing function fitting can be solved by training ...
In this paper, we aim to learn a mapping (or embedding) from images to a compact binary space in which Hamming distances correspond to a ranking measure for the image retrieval task. ...
After the deep hash functions are learned, we generate 128 bits hash codes for each input face image for fast face retrieval. The definitions of CMC, FNIR and FPIR are explained in [11, 31] . ...
arXiv:1603.02844v2
fatcat:j7adme72nbc2znonmvqnpsj3ua
A Survey on Image Retrieval Techniques
[chapter]
2020
Advances in Parallel Computing
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become the best approach for image retrieval with number of layers applicable for large database. ...
This paper is presented with the survey of different Image retrieval techniques which used various techniques from visual features to the latest deep learning with Convolutional Neural Network(CNN) which ...
[3] suggested a Deep Self-Taught hashing (DSTH) method in order to overcome some of the problems faced by existing image retrieval methods. ...
doi:10.3233/apc200174
fatcat:dndecfaujba4plwus45mbiyyre
Fast Training of Triplet-Based Deep Binary Embedding Networks
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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In the second stage we propose to map the original image to compact binary codes via carefully designed deep convolutional neural networks (CNNs) and the hashing function fitting can be solved by training ...
In this paper, we aim to learn a mapping (or embedding) from images to a compact binary space in which Hamming distances correspond to a ranking measure for the image retrieval task. ...
After the deep hash functions are learned, we generate 128 bits hash codes for each input face image for fast face retrieval. The definitions of CMC, FNIR and FPIR are explained in [11, 31] . ...
doi:10.1109/cvpr.2016.641
dblp:conf/cvpr/ZhuangLSR16
fatcat:rusgv63odvgr5j3xk62c6f5ywq
2021 Index IEEE Transactions on Multimedia Vol. 23
2021
IEEE transactions on multimedia
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Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name. ...
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. ...
., +, TMM 2021 2561-2574 Multi-Channel Deep Networks for Block-Based Image Compressive Sensing. ...
doi:10.1109/tmm.2022.3141947
fatcat:lil2nf3vd5ehbfgtslulu7y3lq
A deep locality-sensitive hashing approach for achieving optimal image retrieval satisfaction
2022
International Journal of Power Electronics and Drive Systems (IJPEDS)
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Besides, combining hashing methods with a deep learning architecture improves the image retrieval time and accuracy. ...
Hash tables are constructed from the extracted features and trained to achieve fast image retrieval. ...
Finally, a triplet ranking loss was designed for optimization. Lin et al. 2017 [29] presented a new discriminative deep hashing (DDH) network for image retrieval. ...
doi:10.11591/ijece.v12i3.pp2526-2538
fatcat:erj3ok7vrrfxhgbmevekvpiiuq
Deep Image Set Hashing
[article]
2016
arXiv
pre-print
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We investigate the set hashing problem by combining both set representation and hashing in a single deep neural network. ...
Learning-based hashing is often used in large scale image retrieval as they provide a compact representation of each sample and the Hamming distance can be used to efficiently compare two samples. ...
Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. ...
arXiv:1606.05381v2
fatcat:3hipj3exwfgdhj56sjbxlv7fsy
Orthonormal Product Quantization Network for Scalable Face Image Retrieval
[article]
2022
arXiv
pre-print
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However, the significant intra-class variations like pose, illumination, and expressions in face images, still pose a challenge for face image retrieval. ...
Existing deep quantization methods provided an efficient solution for large-scale image retrieval. ...
Previous deep hashing works on face image retrieval mainly focused on the design of the network architecture and widely adopted softmax classification loss for supervision. ...
arXiv:2107.00327v3
fatcat:stsmopaqibf7hkgyr6ewgra3mm
An Efficient Retrieval System Framework for Fabrics Based on Fine-Grained Similarity
2022
Entropy
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To overcome the problems of probabilistic missing and difficult training in classical hashing, we introduce a variational network module and structural module into the hashing model, which is called DVSH ...
Then, a convolutional neural network with a compact structure and cross-domain connections is designed to narrow the gap between fabric images and similarities. ...
[9] directly used the convolutional layer in a CNN as the index for the image, and its excellent retrieval performance demonstrated the superiority of deep CNN for image retrieval. ...
doi:10.3390/e24091319
pmid:36141205
pmcid:PMC9497872
fatcat:23qnou6oerbbfbhvh7qvuuepdu
A Survey on Deep Learning-Driven Remote Sensing Image Scene Understanding: Scene Classification, Scene Retrieval and Scene-Guided Object Detection
2019
Applied Sciences
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RSISU includes the following sub-tasks: remote sensing image scene classification, remote sensing image scene retrieval, and scene-driven remote sensing image object detection. ...
Similar to other domains (e.g., speech recognition and natural image recognition), deep learning has also become the state-of-the-art technique in RSISU. ...
The deep hashing neural networks [81] (i.e., DHNNs) are developed, while both deep feature learning and hash learning neural networks are constructed and linked. ...
doi:10.3390/app9102110
fatcat:oj3acgbmwnhzppxvvjbsn5cfzq
Table of Contents
2021
IEEE transactions on multimedia
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Shao, and J. Lyu Multimedia Search and Retrieval Deep Loss Driven Multi-Scale Hashing Based on Pyramid Connected Network . . . . . ...L. Gu, J. Liu, X. Liu, and J. ...
Huang Multimedia Search and Retrieval Online Hashing With Bit Selection for Image Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Z. Weng and Y. ...
doi:10.1109/tmm.2021.3132246
fatcat:el7u2udtybddrpbl5gxkvfricy
AutoRet: A Self-Supervised Spatial Recurrent Network for Content-Based Image Retrieval
2022
Sensors
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In this paper, we introduce AutoRet, a deep convolutional neural network (DCNN) based self-supervised image retrieval system. The system is trained on pairwise constraints. ...
Further, the embeddings are fused for quality information used for the image retrieval process. The method is benchmarked with three different datasets. ...
Face retrieval [6] systems query for similar facial images for a given query image. Product retrieval [7] systems can identify users' cherished products from online shopping. ...
doi:10.3390/s22062188
pmid:35336358
pmcid:PMC8954462
fatcat:7sh5ffhowzd6fhjbo6zlrss2jm
Vision Transformer Based Video Hashing Retrieval for Tracing the Source of Fake Videos
[article]
2022
arXiv
pre-print
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To solve the above problems, we designed a novel loss Hash Triplet Loss. ...
We use an improved retrieval method to find the original video, named ViTHash. ...
We would like to thank Zewen Long and Xiaowei Yi for the help with the preparation of experimental materials. ...
arXiv:2112.08117v2
fatcat:hxdhsy74e5aqpgxj3mt4kxiddu
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