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Large-scale Image Retrieval using Neural Net Descriptors
2015
Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '15
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Keywords metric indexing; deep convolutional neural network; contentbased image retrieval; k-NN search ONLINE IMAGE RETRIEVAL SYSTEM One of current big challenges in computer science is development of ...
in real time and on a large scale. ...
Visual Search with Deep Neural Networks Recently, the successful application of deep convolutional neural networks revolutionized the area of image and video recognition. ...
doi:10.1145/2766462.2767868
dblp:conf/sigir/NovakBZ15
fatcat:ehofgbfl5ra6jagjbe52ytyojm
Large Scale Deep Convolutional Neural Network Features Search with Lucene
[article]
2016
arXiv
pre-print
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2016 pre-print arXiv:1603.09687v3 fatcat:ef6vruvykbhafctm3fg2bubzjaOther Versions
In this work, we propose an approach to index Deep Convolutional Neural Network Features to support efficient content-based retrieval on large image databases. ...
In order to show that our approach is able to handle large datasets, we have developed a web-based prototype that provides an interface for combined textual and visual searching into a dataset of about ...
Text Representation for Deep Convolutional Neural Network Features In this paper, we propose to index DCNN features using a text encoding that allows us to use a text retrieval engine to perform image ...
arXiv:1603.09687v4
fatcat:az7f3irxmzd3bhhxvp7bdzxhq4
Large-Scale Video Retrieval via Deep Local Convolutional Features
2020
Advances in Multimedia
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A novel framework based on convolutional neural networks (CNNs) is proposed to perform large-scale video retrieval with low storage cost and high search efficiency. ...
The feature aggregation strategy adopts average pooling to encode deep local convolutional features followed by coarse-to-fine retrieval, which allows rapid retrieval in the large-scale video database. ...
Subsequently, the feature representation of the key frame is learned by the pretrained deep convolutional neural networks. ...
doi:10.1155/2020/7862894
fatcat:i3goway7lrhunpmrw7azqjbpcy
Deep learning hashing for mobile visual search
2017
EURASIP Journal on Image and Video Processing
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In this paper, we explore to holistically exploit the deep learning-based hashing methods for more robust and instant mobile visual search. ...
Finally, with the comprehensive study, we discuss the open issues and future research directions of deep learning hashing for mobile visual search. ...
First of all, these works train the deep neural network on large scale image datasets to learn the effective features for image search. ...
doi:10.1186/s13640-017-0167-4
fatcat:vcdhjjbe6jai7hyigxstihcega
Scalable Database Indexing and Fast Image Retrieval Based on Deep Learning and Hierarchically Nested Structure Applied to Remote Sensing and Plant Biology
2019
Journal of Imaging
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We utilize a deep Convolutional Neural Network (CNN) model as a feature extractor to derive deep feature representations from the imaging data. ...
This paper investigates a highly scalable and computationally efficient image retrieval system for real-time content-based searching through large-scale image repositories in the domain of remote sensing ...
Acknowledgments: Rothamsted Research receives support from the Biotechnology and Biological Sciences Research Council (BBSRC) of the UK as part of the Designing Future Wheat (BBS/E/C/000I0220) project) ...
doi:10.3390/jimaging5030033
pmid:34460461
fatcat:62appjmb75cwvaotuve6pbmuki
A Survey on Content Based Image Retrieval Using Convolutional Neural Networks
2020
International Journal of Advanced Trends in Computer Science and Engineering
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It also focuses on content based image retrieval technique (CBIR), with an unsupervised learning method using convolutional Neural Networks (CNN). ...
But the delivery of high quality pictures without human interaction, using automated annotation among very large scale image databases, is still a continuous research process. ...
Deep Convolutional Neural Networks Convolutional neural networks (CNN) [28] are based on ANN and used to enable categorization and clustering of images by their similarities and also to perform object ...
doi:10.30534/ijatcse/2020/70952020
fatcat:vjpq2j2pdza5di426baglhavai
Coarse and fine-grained automatic cropping deep convolutional neural network
[article]
2020
arXiv
pre-print
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First, cluster the intermediate feature maps of the convolutional neural network to obtain the network structure after coarse-grained clipping, and then use the particle swarm optimization algorithm to ...
The existing convolutional neural network pruning algorithms can be divided into two categories: coarse-grained clipping and fine-grained clipping. ...
Related work Deep convolutional neural networks have made major breakthroughs in various fields of images, but their huge parameters and floating point operations limit the network's operation on mobile ...
arXiv:2010.06379v2
fatcat:b4rgyq3kpzbipngjmzbpuqkxni
Using Convolution Neural Network for Defective Image Classification of Industrial Components
2021
Mobile Information Systems
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For this purpose, a pretrained convolution neural network based on the PyTorch framework is employed to extract discriminating features from the dataset, which is then used for the classification task. ...
In large-scale manufacturing, industrial cameras are utilized to take constant images of components for several reasons. ...
Because of the extensive application of deep learning in image processing, this paper uses convolution neural networks and other models in deep learning to classify and detect defects in large-scale industrial ...
doi:10.1155/2021/9092589
fatcat:sep3lpggzfbglfbjuofu3cysry
Satellite Imagery Classification Based On Deep Convolution Network
2016
Zenodo
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In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. ...
The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at ...
all based on deep convolutional neural networks (DCNN). ...
doi:10.5281/zenodo.1125018
fatcat:tky2xdnbf5auzlcgjm3vfta7ue
Deep understanding of 3-D multimedia information retrieval on social media: implications and challenges
2019
Iran Journal of Computer Science
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With the recent penetration and proliferation of social networks into our lives, human choices and preferences have become more socially accessible. ...
The big explosion of multimedia data on the web has enabled social networks to gauge user likes, dislikes, and needs. ...
SA-BoF also requires testing on 3D large-scale shape benchmarks 6 On-the-fly learning for visual search of large-scale image and video datasets [66] Proposes to enable run-time visual search of large-scale ...
doi:10.1007/s42044-019-00030-5
fatcat:e7kgskeqxbaznbjh3hrmhw3nke
Intelligent and Secure Content-based Image Retrieval for Mobile Users
2019
IEEE Access
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INDEX TERMS Content-based image retrieval, convolutional neural network (CNN), lattice-based homomorphic scheme. ...
We implement a real number computation mechanism and a divide-and-conquer CNN evaluation protocol to enable our framework to securely and efficiently evaluate the deep CNN with a large number of inputs ...
CONVOLUTIONAL NEURAL NETWORK (CNN) Convolutional Neural Network (CNN) [27] is a deep artificial neural network, which has been proven very effective in areas such as image classification and objects ...
doi:10.1109/access.2019.2935222
fatcat:nkfx7muerrcwhkijh4cre3e22i
PSTNet: Crowd Flow Prediction by Pyramidal Spatio-Temporal Network
2021
IEICE transactions on information and systems
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, neural architecture search ...
to fuse the heterogeneous spatiotemporal features of multiple scales. ...
With great achievement of deep learning algorithms, crowd flow prediction is proved to make breakthrough progresses by convolutional neural network (CNN). According to Zhang et al. [2] . ...
doi:10.1587/transinf.2020edl8111
fatcat:ll4vppif5vbipjg767ymqjucpa
Price change prediction of ultra high frequency financial data based on temporal convolutional network
[article]
2021
arXiv
pre-print
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Then, temporal convolutional network (TCN) is utilized to predict the conditional probability for each category. ...
In addition, the scale of the dataset reached nearly 10 million, to the best of our knowledge, there has been no previous attempt to apply TCN to such a large-scale UHF transaction price dataset in Chinese ...
Acknowledgements This research was partly supported by National Natural Science Foundation of China (No.71771204) and the Fundamental Research Funds for the Central Universities. ...
arXiv:2107.00261v1
fatcat:gwoih4iorbg4nfw4dgyyybaspe
Survival study on cyclone prediction methods with remote sensing images
2022
International Journal of Health Sciences
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Among the features, it is essential one to find in which direction cyclone travels and it influence the areas increasing the damage to life and assets. ...
It included the number of interrelated features like eye, cyclone pathway, wind speed, generated storm surges, rainfall intensity and so on. ...
Multiscale fusion features on varied scales were built by deep convolutional network to process multiple clouds. ...
doi:10.53730/ijhs.v6ns1.6668
fatcat:g4gdygi7l5el5eynoqgvebvmze
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. ...
Nowadays the concept of search engines relies on deeper vision of content and perhaps interested in providing the results effectively by employing several algorithms from the areas like machine learning ...
Convolutional Network Using Siamese CNN Architecture: The Deep convolutional network includes fusing two convolutional neural networks(CNN)'s consisting of a single loss function and the method includes ...
doi:10.4108/eai.21-12-2018.159334
fatcat:2wjongwrhrfflm2amb3zyd52b4
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