Learning from error: A two-level combined model for image classification

2011 ◽  
Author(s):  
Mingyang Jiang ◽  
Chunxiao Li ◽  
Zirui Deng ◽  
Jufu Feng ◽  
Liwei Wang
Keyword(s):  
Image Classification ◽  
Combined Model ◽  
Learning From Error

Performance Prediction of Asphalt Pavement Based on the Combined Model

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Wenwen Feng ◽  
Guanglai Jin ◽  
Haiting Liu ◽  
Zhixiang Zhang
Keyword(s):  
Performance Prediction ◽  
Asphalt Pavement ◽  
Combined Model

USING FUZZY CLUSTERING IN STRUCTURAL METHODS OF IMAGE CLASSIFICATION

2020 ◽  
Vol 79 (9) ◽  
pp. 781-791
Author(s):  
V. О. Gorokhovatskyi ◽  
I. S. Tvoroshenko ◽  
N. V. Vlasenko
Keyword(s):  
Image Classification ◽  
Fuzzy Clustering ◽  
Structural Methods

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

FLOWS AT VIADROSTRAGUVANNI BASED ON COMBINED MODEL

2017 ◽  
Vol 23 (5(1)) ◽  
pp. 87-97
Author(s):  
T. Misyura ◽  
V. Zavialov ◽  
O. Lobok ◽  
N. Popova ◽  
Y. Zaporozhets
Keyword(s):  
Combined Model

Iteration Based Polarimetric SAR Image Classification

PIERS Online ◽  
2007 ◽  
Vol 3 (5) ◽  
pp. 625-628
Author(s):  
Jian Yang ◽  
Xiaoli She ◽  
Tao Xiong
Keyword(s):  
Image Classification ◽  
Sar Image ◽  
Polarimetric Sar
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