FastNet: A New Platform for Creating and Exploring Large-Scale Internet Databases from Images – The development of deep neural networks have enabled powerful machine learning tools and deep learning technologies to provide a fast and accurate understanding of complex images. Since deep neural networks are extremely accurate at a large number of iterations, they have been widely used in various image databases. This paper presents the first comprehensive overview of how deep neural networks can be used for object recognition at scale. At the core of this article is the recognition, by means of deep neural network models, of the human body. Furthermore, the recognition of the human body can be used to provide a new dataset for image retrieval, i.e. a 3D representation of an organism. Experiments with different datasets show that the recognition of human body is significantly faster and has been confirmed in real life scenarios.
Automatic object categorization on high-resolution images can help in the identification of objects of interest, but the most basic method is to first learn the category of images to classify. In this paper, we investigate the use of deep convolutional networks for object categorization. Deep neural networks are a new class of networks that is not only trained on high-resolution images but also has different architectures compared to the convolutional-convolutional networks, and have been trained on non-resolution images. We show a promising result on image categorization.
The Bregman-Ludacache dyadic random field hypothesis testing framework
FastNet: A New Platform for Creating and Exploring Large-Scale Internet Databases from Images
A Comprehensive Evaluation of BDA in Multilayer Human Dataset
Convolutional-Neural-Network for Image AnalysisAutomatic object categorization on high-resolution images can help in the identification of objects of interest, but the most basic method is to first learn the category of images to classify. In this paper, we investigate the use of deep convolutional networks for object categorization. Deep neural networks are a new class of networks that is not only trained on high-resolution images but also has different architectures compared to the convolutional-convolutional networks, and have been trained on non-resolution images. We show a promising result on image categorization.