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A disease category feature database construction method of brain image based on deep convolutional neural network
2020
PLoS ONE
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Based on the deep convolutional neural network, an image classifier applicable to brain disease was designed to distinguish between the image features of the different brain diseases with similar anatomical ...
category feature database of brain image was tested and evaluated through large numbers of pathological image retrieval experiments, the accuracy and the effectiveness of the proposed approach was verified ...
In spite of a large amount of studies recently carried out on deep convolutional neural networks for the classification of color images, when these classic models are directly applied to medical images ...
doi:10.1371/journal.pone.0232791
pmid:32479504
pmcid:PMC7263580
fatcat:mpauqndgebggjey5vy54t562be
Large Scale Multimedia Management: A Comprehensive Review
2022
Information
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In this context, large-scale image retrieval is a fundamental task. ...
More recently, these methods based on convolutional neural networks (CNNs) for feature extraction and image classification are widely used. ...
Large Scale Multimedia Management Method: Overview In this section, we will briefly present and discuss some recent works in image retrieval for large scale databases. ...
doi:10.3390/info13010028
fatcat:okdnlufv75bgpdnuiph6k22k2u
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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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. ...
It also focuses on content based image retrieval technique (CBIR), with an unsupervised learning method using convolutional Neural Networks (CNN). ...
One of the frameworks called Content Based Image Retrieval System (CBIR) [6] [13] [14] , is used in visual contexts to search for similar images among large scale image databases like Flickr [15] ...
doi:10.30534/ijatcse/2020/70952020
fatcat:vjpq2j2pdza5di426baglhavai
Large-Scale Image Retrieval with Attentive Deep Local Features
[article]
2018
arXiv
pre-print
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We propose an attentive local feature descriptor suitable for large-scale image retrieval, referred to as DELF (DEep Local Feature). ...
The new feature is based on convolutional neural networks, which are trained only with image-level annotations on a landmark image dataset. ...
On the right, we illustrate our large-scale feature-based retrieval pipeline. DELF for database images are indexed offline. ...
arXiv:1612.06321v4
fatcat:tadtcno7rbhojnz27qxugojvmi
Toward Fine-grained Image Retrieval with Adaptive Deep Learning for Cultural Heritage Image
2023
Computer systems science and engineering
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This study proposes a cultural heritage content retrieval method using adaptive deep learning for fine-grained image retrieval. ...
The proposed method for retrieving the correct image from a database can deliver an average accuracy of 85 percent. ...
Several researchers have proposed techniques for image retrieval from a large database. Uday et al. ...
doi:10.32604/csse.2023.025293
fatcat:ggtexahsv5fr5ax2cpz2hnsa6u
Texture Synthesis Guided Deep Hashing for Texture Image Retrieval
[article]
2019
arXiv
pre-print
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However, none of the existing works uses hashing to address texture image retrieval mostly because of the lack of sufficiently large texture image databases. ...
With the large-scale explosion of images and videos over the internet, efficient hashing methods have been developed to facilitate memory and time efficient retrieval of similar images. ...
Firstly, due to the lack of large scale texture databases, this task is much less explored in the context of deep learning as compared to ordinary image retrieval. ...
arXiv:1811.01401v4
fatcat:vicohipj5zbhdnrcmua5r42cdu
Convolutional neural networks and hash learning for feature extraction and of fast retrieval of pulmonary nodules
2018
Computer Science and Information Systems
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Using deep convolution neural network (CNN) to construct the CBMIR system can fully characterize the high level semantic features information for medical image retrieval. ...
This causes difficulty in managing and querying these large databases leading to the need of content based medical image retrieval (CBMIR) systems. ...
In CBIR, the features extracted based on image content is used for the image retrieval of large database. ...
doi:10.2298/csis171210020q
fatcat:t7hons6mjfevncwqaomsx3pqxq
Revisiting IM2GPS in the Deep Learning Era
[article]
2017
arXiv
pre-print
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Training with classification loss outperforms several deep feature learning methods (e.g. Siamese networks with contrastive of triplet loss) more typical for retrieval applications. ...
for a given image. ...
This requires learning a representation for comparing images (for which we will use deep learning) and indexing a large reference database. ...
arXiv:1705.04838v1
fatcat:oqbemxdx7bdmrf7eugsoqcjf5m
Optimized Deep-Neural Network for Content-based Medical Image Retrieval in a Brownfield IoMT Network
2022
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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In this paper, a brownfield Internet of Medical Things network is introduced for imaging data that can be easily scaled out depending on the objectives, functional requirements and the number of facilities ...
This is further used to develop a novel Content-based Medical Image Retrieval framework. The developed framework uses DenseNet-201 architecture for generating the image descriptors. ...
in searching and retrieving the most similar images from large-scale datasets. ...
doi:10.1145/3546194
fatcat:tcfb7fqalbfv3h5msc2u7aaw5e
Tiny Descriptors for Image Retrieval with Unsupervised Triplet Hashing
2016
2016 Data Compression Conference (DCC)
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Following the recent successes of Deep Convolutional Neural Networks (DCNN) for large scale image classification, descriptors extracted from DCNNs are increasingly used in place of the traditional hand ...
With internet-scale image databases, like the recently released Yahoo 100M image database [11] , compact global descriptors will be key to fast web-scale image-retrieval. ...
training large and deep networks possible. ...
doi:10.1109/dcc.2016.23
dblp:conf/dcc/LinMPCV16
fatcat:qxdp7hxcpbe6ffk3ck5jta45pu
Tiny Descriptors for Image Retrieval with Unsupervised Triplet Hashing
[article]
2015
arXiv
pre-print
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Following the recent successes of Deep Convolutional Neural Networks (DCNN) for large scale image classification, descriptors extracted from DCNNs are increasingly used in place of the traditional hand ...
A typical image retrieval pipeline starts with the comparison of global descriptors from a large database to find a short list of candidate matches. ...
With internet-scale image databases, like the recently released Yahoo 100M image database [11] , compact global descriptors will be key to fast web-scale image-retrieval. ...
arXiv:1511.03055v1
fatcat:owk7tvr3ibectc6f5u2knokggi
Large-scale retrieval for medical image analytics: A comprehensive review
2018
Medical Image Analysis
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Specifically, we first present the general pipeline of large-scale retrieval, summarize the challenges/opportunities of medical image analytics on a large-scale. ...
In this paper, we review state-of-the-art approaches for large-scale medical image analysis, which are mainly based on recent advances in computer vision, machine learning and information retrieval. ...
For large-scale medical image analytics, learned feature representations 480 are a clear trend, since more and more images are available to train the 481 deep neural networks. ...
doi:10.1016/j.media.2017.09.007
pmid:29031831
fatcat:s6jnxawnongufgdngpjeifv3vm
Similarity Preserving Deep Asymmetric Quantization for Image Retrieval
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Quantization has been widely adopted for large-scale multimedia retrieval due to its effectiveness of coding highdimensional data. ...
However, training the deep models given a large-scale database is highly time-consuming as a large amount of parameters are involved. ...
As a large amount of parameters is needed for convolution networks, training deep quantization models given a large-scale database is time-consuming. ...
doi:10.1609/aaai.v33i01.33018183
fatcat:jepdc4y5uvclrhllnibtlaa4z4
Large-scale image analysis using docker sandboxing
[article]
2017
arXiv
pre-print
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With the advent of specialized hardware such as Graphics Processing Units (GPUs), large scale image localization, classification and retrieval have seen increased prevalence. ...
., searching for multiple products in an image by combining image localisation and retrieval. ...
Introduction Large-scale image retrieval has been a mainstay for both academic research and commercial products for several years [2, 17, 18] . ...
arXiv:1703.02898v1
fatcat:p6je3qzfmzch3dkhlv5tkjnlna
Geometrically transformed image retrieval with transfer learning
2022
International Journal of Health Sciences
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In this paper, we propose to use features pre-trained CNN model combinations, which trained for large image database containing rotated as well as scaled images for classification & similar image retrieval ...
For image processing, classification and retrieval deep learning (DL) techniques utilized as state-of-the art techniques. For the images geometric transformation especially, rotation is common thing. ...
As well as the image first go to OAD model and then deep learning model for similar image retrieval which will further increase processing time. ...
doi:10.53730/ijhs.v6ns2.8024
fatcat:ccre4v3mkzbqzmhfsjt3gl3j5i
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