Structured optimal graph based sparse feature extraction for semi-supervised learning

2020 ◽  
Vol 170 ◽  
pp. 107456 ◽  
Author(s):  
Zhonghua Liu ◽  
Zhihui Lai ◽  
Weihua Ou ◽  
Kaibing Zhang ◽  
Ruijuan Zheng
Keyword(s):  
Feature Extraction ◽  
Supervised Learning ◽  
Optimal Graph

scholarly journals Target Detection Network for SAR Images Based on Semi-Supervised Learning and Attention Mechanism

2021 ◽  
Vol 13 (14) ◽  
pp. 2686
Author(s):  
Di Wei ◽  
Yuang Du ◽  
Lan Du ◽  
Lu Li
Keyword(s):  
Feature Extraction ◽  
Supervised Learning ◽  
Target Detection ◽  
Scene Recognition ◽  
Attention Mechanism ◽  
Sar Image ◽  
Target Level ◽  
Sar Images ◽  
Training Samples ◽  
Pass Through

The existing Synthetic Aperture Radar (SAR) image target detection methods based on convolutional neural networks (CNNs) have achieved remarkable performance, but these methods require a large number of target-level labeled training samples to train the network. Moreover, some clutter is very similar to targets in SAR images with complex scenes, making the target detection task very difficult. Therefore, a SAR target detection network based on a semi-supervised learning and attention mechanism is proposed in this paper. Since the image-level label simply marks whether the image contains the target of interest or not, which is easier to be labeled than the target-level label, the proposed method uses a small number of target-level labeled training samples and a large number of image-level labeled training samples to train the network with a semi-supervised learning algorithm. The proposed network consists of a detection branch and a scene recognition branch with a feature extraction module and an attention module shared between these two branches. The feature extraction module can extract the deep features of the input SAR images, and the attention module can guide the network to focus on the target of interest while suppressing the clutter. During the semi-supervised learning process, the target-level labeled training samples will pass through the detection branch, while the image-level labeled training samples will pass through the scene recognition branch. During the test process, considering the help of global scene information in SAR images for detection, a novel coarse-to-fine detection procedure is proposed. After the coarse scene recognition determining whether the input SAR image contains the target of interest or not, the fine target detection is performed on the image that may contain the target. The experimental results based on the measured SAR dataset demonstrate that the proposed method can achieve better performance than the existing methods.

Damageless Watermark Extraction Using Nonlinear Feature Extraction Scheme Trained on Frequency Domain

2011 ◽  
pp. 117-142
Author(s):  
Kensuke Naoe ◽  
Yoshiyasu Takefuji
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Frequency Domain ◽  
Supervised Learning ◽  
The Neural Network ◽  
New Information ◽  
Extraction Scheme ◽  
Trained Neural Network ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

In this chapter, we propose a new information hiding and extracting method without embedding any information into the target content by using a nonlinear feature extraction scheme trained on frequency domain. The proposed method can detect hidden bit patterns from the content by processing the coefficients of the selected feature subblocks to the trained neural network. The coefficients are taken from the frequency domain of the decomposed target content by frequency transform. The bit patterns are retrieved from the network only with the proper extraction keys provided. The extraction keys, in the proposed method, are the coordinates of the selected feature subblocks and the neural network weights generated by the supervised learning of the neural network. The supervised learning uses the coefficients of the selected feature subblocks as the set of input values, and the hidden bit patterns are used as the teacher signal values of the neural network, which is the watermark signal in the proposed method. With our proposed method, we are able to introduce a watermark scheme with no damage to the target content.

COVID-19 Detection Using Feature Extraction and Semi-Supervised Learning from Chest X-ray Images

2021 ◽  
Author(s):  
Samiul Haque ◽  
Mohammad Akidul Hoque ◽  
Mohammad Ariful Islam Khan ◽  
Sabbir Ahmed
Keyword(s):  
Feature Extraction ◽  
Supervised Learning ◽  
X Ray ◽  
Chest X Ray

scholarly journals A Strictly Unsupervised Deep Learning Method for HEp-2 Cell Image Classification

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2717
Author(s):  
Caleb Vununu ◽  
Suk-Hwan Lee ◽  
Ki-Ryong Kwon
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Feature Learning ◽  
Automated Classification ◽  
Benchmark Datasets ◽  
Unsupervised Deep Learning ◽  
Latent Representations

Classifying the images that portray the Human Epithelial cells of type 2 (HEp-2) represents one of the most important steps in the diagnosis procedure of autoimmune diseases. Performing this classification manually represents an extremely complicated task due to the heterogeneity of these cellular images. Hence, an automated classification scheme appears to be necessary. However, the majority of the available methods prefer to utilize the supervised learning approach for this problem. The need for thousands of images labelled manually can represent a difficulty with this approach. The first contribution of this work is to demonstrate that classifying HEp-2 cell images can also be done using the unsupervised learning paradigm. Unlike the majority of the existing methods, we propose here a deep learning scheme that performs both the feature extraction and the cells’ discrimination through an end-to-end unsupervised paradigm. We propose the use of a deep convolutional autoencoder (DCAE) that performs feature extraction via an encoding–decoding scheme. At the same time, we embed in the network a clustering layer whose purpose is to automatically discriminate, during the feature learning process, the latent representations produced by the DCAE. Furthermore, we investigate how the quality of the network’s reconstruction can affect the quality of the produced representations. We have investigated the effectiveness of our method on some benchmark datasets and we demonstrate here that the unsupervised learning, when done properly, performs at the same level as the actual supervised learning-based state-of-the-art methods in terms of accuracy.

scholarly journals Vulnerability Recognition and Resurgence in Network based on Prediction Model and Cognitive based Elucidation

2021 ◽  
Vol 2070 (1) ◽  
pp. 012122
Author(s):  
Jayaganesh Jagannathan ◽  
M.Y. Mohamed Parvees
Keyword(s):  
Feature Extraction ◽  
Prediction Model ◽  
Supervised Learning ◽  
Learning Model ◽  
Network Interface ◽  
Flow Of Information

Abstract The challengeable factor in current network issue is identification of vulnerability and the same can be prevented before it occurs. Many traditional measures applied to keep track of assessing the system in terms of misconduct calculation. There are two different kind application running via network interface which are known process application and unknown process application. Known applications can be managed with the help of existing approaches whereas resolving problems of unknown applications are questionable. The proposed solution addresses this issue by applying cognitive based solutions and supervised learning model. Traffic parameters considered here as major concern and feature extraction is done against parameters flow of information does after pre-process the data. Training, Automation and detection is a sequence of process is used to find vulnerability misconduct in network and simulated with the help of python.

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