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A Novel Information retrieval system for distributed cloud using Hybrid Deep Fuzzy Hashing Algorithm

Dr. V. Suma
2020 Journal of Information Technology and Digital World  
Hashing efficiently retrieves the information based on mapping the similar information as correlated binary codes and this underlying information is trained using deep neural network and fuzzy logic to  ...  Considering these issues also to improve the retrieval performance, a hybrid deep fuzzy hashing algorithm is introduced in this research work.  ...  Mathematical formulation for the hashing approach, deep fuzzy for information retrieval are systematically obtained to improve the retrieval efficiency and security of the data in distributed cloud environment  ... 
doi:10.36548/jitdw.2020.3.003 fatcat:j26ri5vzqvcvtiwpi7u4eybfjm

A Novel Image Retrieval Method with Improved DCNN and Hash

Yan Zhou, Lili Pan, Rongyu Chen, Weizhi Shao
2020 Journal of Information Hiding and Privacy Protection  
realizes efficient image retrieval.  ...  However, the deep feature dimensions obtained by Deep Convolutional Neural Network (DCNN) are too high and redundant, which leads to low retrieval efficiency.  ...  To further enhance the retrieval efficiency and reduce the storage requirements, we hash the deep features of the image.  ... 
doi:10.32604/jihpp.2020.010486 fatcat:qgja7jefynavfl4zhufhut6dym

Special issue of 2017 India International Congress on Computational Intelligence

Suash Deb, Ka-Chun Wong, Thomas Hanne
2020 Neural computing & applications (Print)  
The first one is the design of Siamese-twin random projection neural network for image hashing; the second one is the bagging tree retrieval algorithm.  ...  The study reported by Fakhr et al. has proposed the integration of Siamese-twin neural network hashing for unsupervised image retrieval. The authors have proposed two methods in the study.  ...  The first one is the design of Siamese-twin random projection neural network for image hashing; the second one is the bagging tree retrieval algorithm.  ... 
doi:10.1007/s00521-020-04890-y fatcat:uz3xkiqu4rdhvef5m3kag5aiay

Intelligent computational techniques for multimodal data

Shishir Kumar, Prabhat Mahanti, Su-Jing Wang
2019 Multimedia tools and applications  
The special issue touched different hot topics related to Computer Vision, Computational Biology, Multimedia data mining, High-dimensional multimedia data, Deep convolution network, Deep semantic preserving  ...  hashing, High-dimensional multimedia classification, Deep CNN and extended residual units, particle swarm optimization, Cyberbullying detection on social multimedia, Multimedia detection algorithm of  ...  Conclusion We hope these contributions will be of interest and value to readers from a wide range of subject areas and form a reference for future development.  ... 
doi:10.1007/s11042-019-07936-z fatcat:icmwanpmgbd77cdv47nminn4ee

FMHash: Deep Hashing of In-Air-Handwriting for User Identification [article]

Duo Lu, Dijiang Huang, Anshul Rai
2019 arXiv   pre-print
Many mobile systems and wearable devices, such as Virtual Reality (VR) or Augmented Reality (AR) headsets, lack a keyboard or touchscreen to type an ID and password for signing into a virtual website.  ...  Although gesture-based authentication has been well-studied, less attention is paid to the gesture-based user identification problem, which is essentially an input method of account ID and an efficient  ...  Such systems build a deep convolutional neural network (CNN) to convert 2D images to compact binary hash codes, and use the hash code to index the image database.  ... 
arXiv:1806.03574v2 fatcat:uw5xnem76bhi5pwcyzkykvsl7y

A Classification Retrieval Method for Encrypted Speech Based on Deep Neural Network and Deep Hashing

Qiuyu Zhang, Xuejiao Zhao, Yinjie Hu
2020 IEEE Access  
[16] proposed a method based on machine learning and neural network, which combines fuzzy logic and probabilistic neural network (PNN) features to form a fuzzy probabilistic neural network (FPNN), to  ...  , a classification retrieval method for encrypted speech based on DNN and deep hashing is proposed.  ... 
doi:10.1109/access.2020.3036048 fatcat:wds5nmzsrzfsxe56zstuejs2fy

2020 Index IEEE Transactions on Multimedia Vol. 22

2020 IEEE transactions on multimedia  
., +, TMM Aug. 2020 2048-2060 Improved Deep Hashing With Soft Pairwise Similarity for Multi-Label Image Retrieval.  ...  ., +, TMM Jan. 2020 229-241 Entropy Improved Deep Hashing With Soft Pairwise Similarity for Multi-Label Image Retrieval.  ...  Image watermarking Blind Watermarking for 3-D Printed Objects by Locally Modifying Layer Thickness. 2780 -2791 Low-Light Image Enhancement With Semi-Decoupled Decomposition.  ... 
doi:10.1109/tmm.2020.3047236 fatcat:llha6qbaandfvkhrzpe5gek6mq

Toward Fine-grained Image Retrieval with Adaptive Deep Learning for Cultural Heritage Image

Sathit Prasomphan
2023 Computer systems science and engineering  
This study proposes a cultural heritage content retrieval method using adaptive deep learning for fine-grained image retrieval.  ...  Adaptive deep learning for fine-grained image retrieval was used to retrieve cultural heritage content, and it outperformed state-of-theart methods in fine-grained image retrieval.  ...  Wu [31] introduced a deep incremental hashing network (DIHN), a new deep hashing system for incrementally learning hash codes.  ... 
doi:10.32604/csse.2023.025293 fatcat:ggtexahsv5fr5ax2cpz2hnsa6u

Deep Hashing Using Proxy Loss on Remote Sensing Image Retrieval

Xue Shan, Pingping Liu, Yifan Wang, Qiuzhan Zhou, Zhen Wang
2021 Remote Sensing  
Naturally, we present a proxy-based hash retrieval method, called DHPL (Deep Hashing using Proxy Loss), which combines hash code learning with proxy-based metric learning in a convolutional neural network  ...  For the aerial image dataset (AID), DHPL achieved an mAP of up to 93.53% on 16 hash bits, 97.36% on 32 hash bits, 98.28% on 48 hash bits, and 98.54% on 64 bits.  ...  Acknowledgments: Thanks to the School of Computer Science and Technology of Jilin University for its support of the experimental equipment.  ... 
doi:10.3390/rs13152924 fatcat:onup5bhghnhlnoii3vabroo6bm

Ternary Hashing [article]

Chang Liu, Lixin Fan, Kam Woh Ng, Yilun Jin, Ce Ju, Tianyu Zhang, Chee Seng Chan, Qiang Yang
2021 arXiv   pre-print
This paper proposes a novel ternary hash encoding for learning to hash methods, which provides a principled more efficient coding scheme with performances better than those of the state-of-the-art binary  ...  Our work demonstrates that, with an efficient implementation of ternary logic on standard binary machines, the proposed ternary hashing is compared favorably to the binary hashing methods with consistent  ...  We have implemented Deep Polarized Network (DPN) [8] , Greedy-Hash (GH) [23] , and Just-Maximizing-Likelihood Hashing (JMLH) [22] for classification-based methods while Hash-Net [5] , Deep Balanced  ... 
arXiv:2103.09173v2 fatcat:xmp4twnnvnhixfejcxyf4aexti

Modeling Perception Errors towards Robust Decision Making in Autonomous Vehicles

Andrea Piazzoni, Jim Cherian, Martin Slavik, Justin Dauwels
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
However, the current evaluation metrics for perception algorithms are typically designed to measure their accuracy per se and do not account for their impact on the decision making subsystem(s).  ...  This limitation does not help developers and third party evaluators to answer a critical question: is the performance of a perception subsystem sufficient for the decision making subsystem to make robust  ...  Introduction With the unprecedented growth of image data, hashing based approximate nearest neighbour (ANN) searching have attracted more and more attention due to their high retrieval efficiency and low  ... 
doi:10.24963/ijcai.2020/479 dblp:conf/ijcai/TuM020 fatcat:dwov2yxchnevtobgzmcuzmyzne

Perlustration on Image Processing under Free Hand Sketch Based Image Retrieval

S. Amarnadh, P.V.G.D. Reddy, N.V.E.S. Murthy
2018 EAI Endorsed Transactions on Internet of Things  
, 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).  ...  The hierarchical K-Medoids based indexing is used for efficient and quick retrieval process from millions of images. Steps: 1.  ... 
doi:10.4108/eai.21-12-2018.159334 fatcat:2wjongwrhrfflm2amb3zyd52b4

An Efficient Hyperspectral Image Retrieval Method: Deep Spectral-Spatial Feature Extraction with DCGAN and Dimensionality Reduction Using t-SNE-Based NM Hashing

Jing Zhang, Lu Chen, Li Zhuo, Xi Liang, Jiafeng Li
2018 Remote Sensing  
How to ensure retrieval accuracy and efficiency is a challenging task in the field of hyperspectral image retrieval. In this paper, an efficient hyperspectral image retrieval method is proposed.  ...  Some researchers have adopted DL networks, such as the Deep Belief Network (DBN) and the Convolutional Neural Network (CNN) to extract deep spectral-spatial features from hyperspectral images [13] [14]  ...  Considering that high-dimensional deep features greatly affect retrieval efficiency, the t-SNE-based NM hashing method is adopted for deep feature dimensionality reduction.  ... 
doi:10.3390/rs10020271 fatcat:ei7mrzq3lvgl3fy6wjjveehbmm

Large-Scale Remote Sensing Image Retrieval Based on Semi-Supervised Adversarial Hashing

Xu Tang, Chao Liu, Jingjing Ma, Xiangrong Zhang, Fang Liu, Licheng Jiao
2019 Remote Sensing  
With the number of RS images increases explosively, not only the retrieval precision but also the retrieval efficiency is emphasized in the large-scale RSIR scenario.  ...  In this paper, we propose a new hash learning method, named semi-supervised deep adversarial hashing (SDAH), to accomplish the ANN for the large-scale RSIR task.  ...  Deep quantization network for efficient image retrieval. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA, 12–17 February 2016. 80.  ... 
doi:10.3390/rs11172055 fatcat:cy3ehjr2ejf7dezr4j4idc4jra

Attention-based end-to-end CNN Framework for Content-Based X-Ray Image Retrieval

2021 Turkish Journal of Electrical Engineering and Computer Sciences  
In hospitals, the need for 8 content-based image retrieval (CBIR) systems is seriously evident in order to store all images effectively and access them 9 quickly when necessary.  ...  4 The widespread use of medical imaging devices allows deep analysis of diseases. However, the task of examining 5 medical images increases the burden of specialist doctors.  ...  Medical image retrieval for detecting pneumonia using binary classification with deep convolutional neural networks.  ... 
doi:10.3906/elk-2105-242 fatcat:bcw473dy7rbetayc6u5um2v62m
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