Deep learning on small datasets using online image search

2016 ◽  
Author(s):  
Martin Kolář ◽  
Michal Hradiš ◽  
Pavel Zemčík
Keyword(s):  
Deep Learning ◽  
Image Search

Fast Image Search with Pixel-Based Deep Learning Framework via Efficient Compact Binary Code and Addictive Latent Layer

2017 ◽  
Vol 32 (03) ◽  
pp. 1859004 ◽  
Author(s):  
Jun Yi Li ◽  
Jian Hua Li
Keyword(s):  
Deep Learning ◽  
Nearest Neighbor ◽  
Feature Learning ◽  
Nearest Neighbor Search ◽  
Feature Representation ◽  
Image Feature ◽  
Retrieval Algorithm ◽  
Image Search ◽  
Learning Framework ◽  
Compact Binary

As we know, the nearest neighbor search is a good and effective method for good-sized image search. This paper mainly introduced how to learn an outstanding image feature representation form and a series of compact binary Hash coding functions under deep learning framework. Our concept is that binary codes can be obtained using a hidden layer to present some latent concepts dominating the class labels with usable data labels. Our method is effective in obtaining hash codes and image representations, so it is suitable for good-sized dataset. It is demonstrated in our experiment that the performances of the proposed algorithms were then verified on three different databases, MNIST, CIFAR-10 and Caltech-101. The experimental results reveal that two-proposed image Hash retrieval algorithm based on pixel-level automatic feature learning show higher search accuracy than the other algorithms; moreover, these two algorithms were proved to be more favorable in scalability and generality.

IMAGE SEARCH USING DEEP LEARNING METHOD

2021 ◽  
Author(s):  
Bui Thanh Hung ◽  
Pham Hoang Phuong
Keyword(s):  
Deep Learning ◽  
Image Search ◽  
Learning Method

Reverse Image Search Improved by Deep Learning

2021 ◽  
Author(s):  
Paras Nath Singh ◽  
Tara P. Gowdar
Keyword(s):  
Deep Learning ◽  
Image Search

scholarly journals Authorized Vehicle Recognition System

2020 ◽  
Author(s):  
Kedar R ◽  
Kaviraj A ◽  
Manish R ◽  
Niteesh B ◽  
Suthir S
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Recognition System ◽  
Learning Object ◽  
Image Search ◽  
Human Beings ◽  
Government Organizations ◽  
Vehicle Recognition ◽  
Character Extraction ◽  
Region Extraction

The technology is growing and increasing in our day to day life to satisfy the needs of human beings. The system we are going to propose makes the human job easier. Here the YOLO algorithm which is a deep learning object detection architecture is used to detect the number plate of the vehicle. After detecting the number plate it converts the vehicle number to a text format. Then it checks it with the database to see if the vehicle is authorized to enter into the premise or not. This system can be implemented in highly restrained areas such as military areas, government organizations, Parliament, etc. This proposed system has around six stages: Capture Image, Search for black pixels, Image filtering, Plate region extraction, character extraction, OCR for character recognition. The alphanumeric characters are identified using the OCR algorithm. It is then used to compare the obtained result from the YOLO algorithm with the database and then check if the vehicle is allowed to enter the premise or not. This proposed system is simulated and implemented using Python, and it was also tested on real-time images for performance purposes.

Text-Based Image Retrieval Using Deep Learning

2021 ◽  
pp. 87-97
Author(s):  
Udit Singhania ◽  
B. K. Tripathy
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Language Processing ◽  
Neural Nets ◽  
Image Search ◽  
Restricted Boltzmann Machines ◽  
Previous Version ◽  
Text Query ◽  
Learning Architectures ◽  
Over Time

This chapter is mainly an advanced version of the previous version of the chapter named “An Insight to Deep Learning Architectures” in the encyclopedia. This chapter mainly focusses on giving the insights of information retrieval after the year 2014, as the earlier part has been discussed in the previous version. Deep learning plays an important role in today's era, and this chapter makes use of such deep learning architectures which have evolved over time and have proved to be efficient in image search/retrieval nowadays. In this chapter, various techniques to solve the problem of natural language processing to process text query are mentioned. Recurrent neural nets, deep restricted Boltzmann machines, general adversarial nets have been discussed seeing how they revolutionize the field of information retrieval.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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