© 2016 IEEE. This paper introduces a dataset of historical images created by the State Library of New South Wales and the University of Technology Sydney (UTS). The dataset has a total of 29713 images with 119 unique labels. Each image contains multiple labels. We use a CNN-based framework to explore the feasibility of our dataset in image multi-labeling and retrieval research, and extract semantic level image features for future research use. The experiment results illustrate that effective deep learning models can be trained on our dataset. We also introduce five applications that can be studied on our historical image dataset
Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, w...
The project assigned was Visual Recognition using Deep Learning. Specifically, this project aims to ...
Graphical Search Engines are conceptually used in many development areas surrounding information ret...
This paper introduces a dataset of historical images created by the State Library of New South Wales...
This research investigates and describes an image search engine for digital history using deep learn...
International audienceHistorical newspaper image analysis is a challenging task due to the complex l...
In recent years, instance-level-image retrieval has attracted massive attention. Several researchers...
The large diffusion of cheap cameras and smartphones led to an exponential daily production of digit...
International audienceThis paper aims to communicate an ongoing research project conducted at Columb...
The aim of this work is to create a system for historical documents classification . The task is spe...
Using Convolutional Neural Networks to Explore Over 400 Years of Book Illustrations: Starting from F...
In 2018, the number of mobile phone users will reach about 4.9 billion. Assuming an aver...
Deep neural networks have shown increasing performance in image classification recent years. However...
Digital humanities research has focused primarily on the analysis of texts. This emphasis stems from...
Labeling of trademark images with Vienna codes from the Vienna classification is a manual process ca...
Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, w...
The project assigned was Visual Recognition using Deep Learning. Specifically, this project aims to ...
Graphical Search Engines are conceptually used in many development areas surrounding information ret...
This paper introduces a dataset of historical images created by the State Library of New South Wales...
This research investigates and describes an image search engine for digital history using deep learn...
International audienceHistorical newspaper image analysis is a challenging task due to the complex l...
In recent years, instance-level-image retrieval has attracted massive attention. Several researchers...
The large diffusion of cheap cameras and smartphones led to an exponential daily production of digit...
International audienceThis paper aims to communicate an ongoing research project conducted at Columb...
The aim of this work is to create a system for historical documents classification . The task is spe...
Using Convolutional Neural Networks to Explore Over 400 Years of Book Illustrations: Starting from F...
In 2018, the number of mobile phone users will reach about 4.9 billion. Assuming an aver...
Deep neural networks have shown increasing performance in image classification recent years. However...
Digital humanities research has focused primarily on the analysis of texts. This emphasis stems from...
Labeling of trademark images with Vienna codes from the Vienna classification is a manual process ca...
Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, w...
The project assigned was Visual Recognition using Deep Learning. Specifically, this project aims to ...
Graphical Search Engines are conceptually used in many development areas surrounding information ret...