Results 41 to 50 of about 26,920 (307)
Angular Deep Supervised Hashing for Image Retrieval
IEEE Access, 2019 Deep learning based image hashing methods learn hash codes by using powerful feature extractors and nonlinear transformations to achieve highly efficient image retrieval. For most end-to-end deep hashing methods, the supervised learning process relies on
Chang Zhou +8 more
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Deep Contrastive Self-Supervised Hashing for Remote Sensing Image Retrieval
Remote Sensing, 2022 Hashing has been widely used for large-scale remote sensing image retrieval due to its outstanding advantages in storage and search speed. Recently, deep hashing methods, which produce discriminative hash codes by building end-to-end deep convolutional ...
Xiaoyan Tan +4 more
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Proceedings of the 26th ACM international conference on Multimedia, 2018 Deep hashing enables image retrieval by end-to-end learning of deep representations and hash codes from training data with pairwise similarity information. Subject to the distribution skewness underlying the similarity information, most existing deep hashing methods may underperform for imbalanced data due to misspecified loss functions.
Cao, Zhangjie +4 more
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A New Subject-Sensitive Hashing Algorithm Based on MultiRes-RCF for Blockchains of HRRS Images
Algorithms, 2022 Aiming at the deficiency that blockchain technology is too sensitive to the binary-level changes of high resolution remote sensing (HRRS) images, we propose a new subject-sensitive hashing algorithm specially for HRRS image blockchains. To implement this
Kaimeng Ding +4 more
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Simultaneous Feature Learning and Hash Coding with Deep Neural Networks [PDF]
, 2015 Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks. For most existing hashing methods, an image is first encoded as a vector of hand-engineering visual features, followed by another ...
Lai, Hanjiang +3 more
core +1 more source
Sequential and Correlated Image Hash Code Generation with Deep Reinforcement Learning
Sakarya University Journal of Computer and Information Sciences, 2023 Image hashing is an algorithm used to represent an image with a unique value. Hashing methods, which are generally developed to search for similar examples of an image, have gained a new dimension with the use of deep network structures and better ...
Can Yüzkollar
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Deep hash: semantic similarity preserved hash scheme
Electronics Letters, 2014 A novel hashing scheme based on a deep network architecture is proposed to tackle semantic similarity problems. The proposed methodology utilises the ability of deep networks to learn nonlinear representations of the input features. The equivalence of the neuron layer and the sigmoid smoothed hash functions is introduced, and by incorporating the ...
Weiguo Feng, Baozhi Jia, Ming Zhu
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Unsupervised Deep Generative Hashing [PDF]
Procedings of the British Machine Vision Conference 2017, 2017 Hashing is regarded as an efficient approach for image retrieval and many other big-data applications. Recently, deep learning frameworks are adopted for image hashing, suggesting an alternative way to formulate the encoding function other than the conventional projections.
Yuming Shen, li Liu, Ling Shao
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Synonym‐based multi‐keyword ranked search with secure k‐NN in 6G network
IET Networks, EarlyView., 2022 Abstract Sixth Generation (6G) integrates the next generation communication systems such as maritime, terrestrial, and aerial to offer robust network and massive device connectivity with ultra‐low latency requirement. The cutting edge technologies such as artificial intelligence, quantum machine learning, and millimetre enable hyper‐connectivity to ...
Deebak Bakkiam David, Fadi Al‐Turjman
wiley +1 more source
Trigonometric words ranking model for spam message classification
IET Networks, EarlyView., 2022 Abstract The significant increase in the volume of fake (spam) messages has led to an urgent need to develop and implement a robust anti‐spam method. Several of the current anti‐spam systems depend mainly on the word order of the message in determining the spam message, which results in the system's inability to predict the correct type of message when
Suha Mohammed Hadi +7 more
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