Convolutional-Neural-Network for Image Analysis – 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.
Recently, deep convolutional neural networks (CNNs) have made great strides towards the image classification task. However, they are not fully capable of representing complex object and scenes. In this paper, we study the problem of the representation of complex object and scene data to improve the classification accuracy. In particular, we propose to model the object and scene features in a recurrent network. In this work, the input images for a convolutional neural network are represented as the input images, and recurrent networks are adapted in a single network for the object and scene data. In this way, the representation of these two-dimensional datasets are preserved in a single model, which enables to transfer the data into a sequential and sequential fashion. On the other hand, an image dataset with 3D object features and 3D scene features are learned in 2D recurrent network model, which has a fixed training and training feature loss. We show that the proposed method is extremely effective at solving the object and scene classification tasks. Experimental results on benchmark datasets have shown the superiority of our model over other deep convolutional-NN implementations.
Efficient Scene-Space Merging with Recurrent Loop Regression
An Efficient Sparse Inference Method for Spatiotemporal Data
Convolutional-Neural-Network for Image Analysis
The Evolution of Lexical Variation: Does Language Matter?
Convolutional neural network-based classification using discriminant textRecently, deep convolutional neural networks (CNNs) have made great strides towards the image classification task. However, they are not fully capable of representing complex object and scenes. In this paper, we study the problem of the representation of complex object and scene data to improve the classification accuracy. In particular, we propose to model the object and scene features in a recurrent network. In this work, the input images for a convolutional neural network are represented as the input images, and recurrent networks are adapted in a single network for the object and scene data. In this way, the representation of these two-dimensional datasets are preserved in a single model, which enables to transfer the data into a sequential and sequential fashion. On the other hand, an image dataset with 3D object features and 3D scene features are learned in 2D recurrent network model, which has a fixed training and training feature loss. We show that the proposed method is extremely effective at solving the object and scene classification tasks. Experimental results on benchmark datasets have shown the superiority of our model over other deep convolutional-NN implementations.