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 the original URL.
The file type is application/pdf.
Filters
Deep Learning Hash for Wireless Multimedia Image Content Security
2018
Security and Communication Networks
Preserved Fulltext
With the explosive growth of the wireless multimedia data on the wireless Internet, a large number of illegal images have been widely disseminated in wireless networks, which seriously endangers the content ...
Finally, comprehensive experiments are conducted to evaluate IDLH method by using CIFAR-10 and Caltech 256 image library datasets, and the results show that the retrieval performance of IDLH method can ...
Acknowledgments The work was funded by the National Natural Science Foundation of China (Grants nos. 61206138 and 61373016). ...
doi:10.1155/2018/8172725
fatcat:lh5jfindm5d6fayemyamv4cyou
An Efficient Approach towards Satellite Image Retrieval using Semantic Mining with Hashing
2016
International Journal of Applied Information Systems
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 2018; you can also visit the original URL.
The file type is application/pdf.
This paper puts forward a semantic-based image retrieval approach along with the advantages of hashing for better feature extraction and precise retrieval. ...
Therefore, an effective and efficient method is required for image retrieval. ...
In 2004,Yadong Mu.et.al presented a novel hashing algorithm known as LAMP .This proposed method generated highquality hash functions using kernel conjuring and weak supervision .The LAMP algorithm creates ...
doi:10.5120/ijais2016451592
fatcat:z7wnpppzlje3tlijtn2rujecg4
Deep Supervised Hashing based on Stable Distribution
2019
IEEE Access
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 the original URL.
The file type is application/pdf.
According to stable distribution, we propose a novel hashing framework to eliminate the discrepancy and support fast image retrieval. ...
Recently, the convolutional neural network (CNN)-based hashing method has achieved its promising performance for image retrieval. ...
any quantization regularizer to achieve effective and efficient large scale image retrieval. ...
doi:10.1109/access.2019.2900489
fatcat:p3mwyez75fc3hlaz7g4bqbz7mm
Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives
[article]
2020
arXiv
pre-print
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 the original URL.
The file type is application/pdf.
Other Versions
This chapter presents recent advances in content based image search and retrieval (CBIR) systems in remote sensing (RS) for fast and accurate information discovery from massive data archives. ...
After discussing their strengths and limitations, we present the deep hashing based CBIR systems that have high time-efficient search capability within huge data archives. ...
As an example, in [8] , [10] kernel-based hashing methods that define hash functions in the kernel space are presented, whereas a partial randomness hashing method that defines the hash functions based ...
arXiv:2004.01613v2
fatcat:d4fjt3vzybbbrejxzobaluqsoq
A Survey on Deep Learning-Driven Remote Sensing Image Scene Understanding: Scene Classification, Scene Retrieval and Scene-Guided Object Detection
2019
Applied Sciences
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 the original URL.
The file type is application/pdf.
To facilitate the sustainable progress of RSISU, this paper presents a comprehensive review of deep-learning-based RSISU methods, and points out some future research directions and potential applications ...
RSISU includes the following sub-tasks: remote sensing image scene classification, remote sensing image scene retrieval, and scene-driven remote sensing image object detection. ...
The first method only uses untagged images, and the hash functions are defined in the kernel space. ...
doi:10.3390/app9102110
fatcat:oj3acgbmwnhzppxvvjbsn5cfzq
Robust discrete code modeling for supervised hashing
2018
Pattern Recognition
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 the original URL.
The file type is application/pdf.
Highlights • We propose a novel supervised hashing scheme to generate high-quality hash codes and hash functions for facilitating large-scale multimedia applications. • We devise an effective binary code ...
Abstract Recent years have witnessed the promising efficacy and efficiency of hashing (also known as binary code learning) for retrieving nearest neighbor in large-scale data collections. ...
Yang Yang and Prof. Fumin Shen.Her research interests including computer vision and deep learning for large-scale image analysis and retrieval projects. ...
doi:10.1016/j.patcog.2017.02.034
fatcat:5n2xtxvg7jh7pofrbcahv33ofm
Discrete Cosine Transform Locality-Sensitive Hashes for Face Retrieval
2014
IEEE transactions on multimedia
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 2015; you can also visit the original URL.
The file type is application/pdf.
Results are shown on a combination of six publicly available face databases (LFW, FERET, FEI, BioID, Multi-PIE, and RaFD). ...
Hashes play the role of keywords: an index is created, and queried to find the images most similar to the query image. Common hash suppression is used to improve retrieval efficiency and accuracy. ...
[9] propose kernel-based supervised hashing, a supervised data-dependent hashing method that effectively learns the kernel-formulated hash functions using the supervised information from the data. ...
doi:10.1109/tmm.2014.2305633
fatcat:bgubdq2avvhbbllowg2slzox3i
A Review of Hashing Methods for Multimodal Retrieval
2020
IEEE Access
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 the original URL.
The file type is application/pdf.
This review clarifies the definition of multimodal retrieval requirements and some related concepts, then introduces some representative hashing methods, mainly supervised methods that make full use of ...
Among many retrieval methods, the hashing method is widely used in multimodal data retrieval due to its low storage cost, fast and effective characteristics. ...
on a large increase in data scale. ...
doi:10.1109/access.2020.2968154
fatcat:e3vmte5hrnhu3b3lf5ws4gwnhm
Hashing Techniques
2017
ACM Computing Surveys
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 2018; you can also visit the original URL.
The file type is application/pdf.
The increasing data volumes impose significant challenges to traditional data analysis tools in storing, processing, and analyzing these extremely large-scale data. ...
This survey reviews and categorizes existing hashing techniques as a taxonomy, in order to provide a comprehensive view of mainstream hashing techniques for different types of data and applications. ...
Such SSH methods have been mainly used for large-scale image search. ...
doi:10.1145/3047307
fatcat:u5asusjs7vdq7f3a6wgnesnodq
A Survey on Big IoT Data Indexing: Potential Solutions, Recent Advancements, and Open Issues
2021
Future Internet
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 the original URL.
The file type is application/pdf.
Open problems and research challenges, e.g., privacy and large-scale data mining, are also discussed. ...
The purpose of this paper is to examine and review existing indexing techniques for large-scale data. ...
The authors also presented a comparative study of multidimensional indexing methods and a comparative study of metric access methods. ...
doi:10.3390/fi14010019
fatcat:xnlzg7cs2fb3lgng65ha5ucf5m
Self-supervised asymmetric deep hashing with margin-scalable constraint
[article]
2021
arXiv
pre-print
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 the original URL.
The file type is application/pdf.
Other Versions
Due to its effectivity and efficiency, deep hashing approaches are widely used for large-scale visual search. ...
In this paper, we propose a novel self-supervised asymmetric deep hashing method with a margin-scalable constraint(SADH) approach to cope with these problems. ...
Acknowledgements This work was supported by the National Natural Science Foundation of China (61806168), Fundamental Research Funds for the Central Universities (SWU117059), and Venture & Innovation Support ...
arXiv:2012.03820v3
fatcat:fscm4ggdyrct3o6kso53mmriou
Multiview Inherent Graph Hashing for Large-Scale Remote Sensing Image Retrieval
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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 the original URL.
The file type is application/pdf.
Hashing first generates a set of short compact hash codes to encode RS images, and then applies hash codes for effective RSIR. ...
Remote sensing image retrieval (RSIR) is one of the most challenging tasks in remote sensing (RS) community. ...
The kernel-based hash functions are adopted to handle linearly inseparable data. Demir and Bruzzone [41] adopt the kernel-based nonlinear hashing method for large-scale RSIR task. ...
doi:10.1109/jstars.2021.3121142
fatcat:7x4inzrcvfae5ou5mplglkm4si
An Efficient Hyperspectral Image Retrieval Method: Deep Spectral-Spatial Feature Extraction with DCGAN and Dimensionality Reduction Using t-SNE-Based NM Hashing
2018
Remote Sensing
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 2018; you can also visit the original URL.
The file type is application/pdf.
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. ...
The other one is the loss of semantic information in a feature after dimensionality reduction with nonlinear manifold learning-based hashing methods. ...
Hyperspectral image retrieval, which aims to find and return the appropriate images from a large database, is an effective and indispensable method for the management of a large amount of hyperspectral ...
doi:10.3390/rs10020271
fatcat:ei7mrzq3lvgl3fy6wjjveehbmm
Low-Rank Hypergraph Hashing for Large-Scale Remote Sensing Image Retrieval
2020
Remote Sensing
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 the original URL.
The file type is application/pdf.
In order to improve hashing performance, we propose a new hash learning method, named low-rank hypergraph hashing (LHH), to accomplish for the large-scale RSIR task. ...
Extensive experiments are conducted on three RS image datasets and one natural image dataset that are publicly available. ...
Hashing Learning Hashing has been a key step to facilitate large-scale image retrieval [24] . ...
doi:10.3390/rs12071164
fatcat:bp7naq2h6natxo3nqlor6xzsjm
Unsupervised Balanced Hash Codes Learning With Multichannel Feature Fusion
2022
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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 the original URL.
The file type is application/pdf.
Unsupervised hashing algorithms are widely used in large-scale remote sensing images (RSIs) retrieval task. ...
Finally, a novel objective function is designed to capture the discrimination and balanced property of hash codes in the hashing learning process. ...
However, these algorithms still have diverse challenges for large-scale RSI retrieval. ...
doi:10.1109/jstars.2022.3162251
fatcat:luqu46czmbfyrds4l6afnaks6q
« Previous
Showing results 1 — 15 out of 865 results