Deep wavelet network for image classification

2016 ◽  
Author(s):  
Salwa Said ◽  
Olfa Jemai ◽  
Salima Hassairi ◽  
Ridha Ejbali ◽  
Mourad Zaied ◽  
...  
Keyword(s):  
Image Classification ◽  
Wavelet Network

PYRAMIDAL HYBRID APPROACH: WAVELET NETWORK WITH OLS ALGORITHM-BASED IMAGE CLASSIFICATION

2011 ◽  
Vol 09 (01) ◽  
pp. 111-130 ◽  
Author(s):  
OLFA JEMAI ◽  
MOURAD ZAIED ◽  
CHOKRI BEN AMAR ◽  
MOHAMED ADEL ALIMI
Keyword(s):  
Least Squares ◽  
Image Classification ◽  
Network Architecture ◽  
Hybrid Approach ◽  
Learning Ability ◽  
Wavelet Network ◽  
Wavelet Networks ◽  
Orthogonal Least Squares ◽  
Colour Image ◽  
Beta Wavelet

Taking advantage of both the scaling property of wavelets and the high learning ability of neural networks, wavelet networks have recently emerged as a powerful tool in many applications in the field of signal processing such as data compression, function approximation as well as image recognition and classification. A novel wavelet network-based method for image classification is presented in this paper. The method combines the Orthogonal Least Squares algorithm (OLS) with the Pyramidal Beta Wavelet Network architecture (PBWN). First, the structure of the Pyramidal Beta Wavelet Network is proposed and the OLS method is used to design it by presetting the widths of the hidden units in PBWN. Then, to enhance the performance of the obtained PBWN, a novel learning algorithm based on orthogonal least squares and frames theory is proposed, in which we use OLS to select the hidden nodes. In the simulation part, the proposed method is employed to classify colour images. Comparisons with some typical wavelet networks are presented and discussed. Simulations also show that the PBWN-orthogonal least squares (PBWN-OLS) algorithm, which combines PBWN with the OLS algorithm, results in better performance for colour image classification.

SAR Image Classification Based on Brushlet and Self-Adaptive Ridgelet Neural Network

2013 ◽  
Vol 347-350 ◽  
pp. 3024-3028
Author(s):  
Nan Zheng ◽  
Hai Feng Tan
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Classification Model ◽  
Image Texture ◽  
Sar Image ◽  
Wavelet Network ◽  
Network Training ◽  
Long Time ◽  
Direction Information ◽  
Self Adaptive

Aiming at the defect that BP neural network classification model takes a long time for network training and the condition that wavelet network model lacks of direction information description, the paper presents a method for SAR image classification based on Brushlet and self-adaptive ridgelet neural network. The method extracts the energy and phase feature of SAR image texture through the Brushlet transformation, and inputs the feature vector that describes the energy and phase information into self-adaptive ridgelet neural network for training and classification. The contrast experiment indicates that the classification method proposed in this paper is rapid and accurate, and outperforms the traditional methods.

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. 

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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