Feature Fusion Using Complex Descriminator

2006 ◽  
pp. 329-350
Author(s):  
David Zhang ◽  
Xiao-Yuan Jing ◽  
Jian Yang
Keyword(s):  
Symmetry Property ◽  
Feature Fusion ◽  
Detailed Comparison ◽  
Comparison Analysis ◽  
Linear Projection ◽  
Analysis Methods ◽  
Projection Analysis ◽  
Complex Linear ◽  
Parallel Feature

This chapter describes feature fusion techniques using complex discriminator. After the introduction, we first introduce serial and parallel feature fusion strategies. Then, the complex linear projection analysis methods, complex PCA and complex LDA, are developed. Next, some feature preprocessing techniques are given. The symmetry property of parallel feature fusion is analyzed and revealed. Then, the proposed methods are applied to biometric applications, related experiments are performed and the detailed comparison analysis is exhibited. Finally, a summary is given.

scholarly journals Analysis Methods Used to Extract Fingerprints Features

2021 ◽  
Vol 38 (3) ◽  
pp. 711-717
Author(s):  
Mohammad S. Khrisat ◽  
Rushdi. S. Abu Zneit ◽  
Hatim Ghazi Zaini ◽  
Ziad A. Alqadi
Keyword(s):  
Detailed Comparison ◽  
Image Features ◽  
Features Extraction ◽  
Color Images ◽  
Comparison Analysis ◽  
Image Rotation ◽  
Analysis Methods ◽  
The Stability

The fingerprint is used in many vital applications important to humans, which requires searching for an effective way to extract the characteristics of the fingerprint. In this paper we will study some of the most popular methods used to extract fingerprints features. For each method the efficiency, accuracy, flexibility and sensitivity for image rotation will be experimentally tested, measured, analyzed in order to give good recommendations of how and when to use a certain method of features extraction. A detailed comparison analysis between MLBP, K_means, WPT, Minutiae methods will be done using several color images in various rotation modes to insure the stability of image features.

scholarly journals Automated Atrial Fibrillation Detection Based on Feature Fusion Using Discriminant Canonical Correlation Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jingjing Shi ◽  
Chao Chen ◽  
Hui Liu ◽  
Yinglong Wang ◽  
Minglei Shu ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Expert Knowledge ◽  
Feature Fusion ◽  
Intraclass Correlation ◽  
Single Feature ◽  
Disability Rate ◽  
Parallel Feature

Atrial fibrillation (AF) is one of the most common cardiovascular diseases, with a high disability rate and mortality rate. The early detection and treatment of atrial fibrillation have great clinical significance. In this paper, a multiple feature fusion is proposed to screen out AF recordings from single lead short electrocardiogram (ECG) recordings. The proposed method uses discriminant canonical correlation analysis (DCCA) feature fusion. It fully takes intraclass correlation and interclass correlation into consideration and solves the problem of computation and information redundancy with simple series or parallel feature fusion. The DCCA integrates traditional features extracted by expert knowledge and deep learning features extracted by the residual network and gated recurrent unit network to improve the low accuracy of a single feature. Based on the Cardiology Challenge 2017 dataset, the experiments are designed to verify the effectiveness of the proposed algorithm. In the experiments, the F1 index can reach 88%. The accuracy, sensitivity, and specificity are 91.7%, 90.4%, and 93.2%, respectively.

JPEG steganalysis based on parallel feature fusion by quaternion

2013 ◽  
Vol 33 (3) ◽  
pp. 663-666
Author(s):  
Fengying HE ◽  
Shangping ZHONG ◽  
Jian YANG
Keyword(s):  
Feature Fusion ◽  
Parallel Feature

scholarly journals COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7286
Author(s):  
Muhammad Attique Khan ◽  
Majed Alhaisoni ◽  
Usman Tariq ◽  
Nazar Hussain ◽  
Abdul Majid ◽  
...  
Keyword(s):  
Deep Learning ◽  
Feature Fusion ◽  
Optimization Method ◽  
Support Vector ◽  
Learning Models ◽  
Multiclass Support Vector Machine ◽  
Firefly Optimization ◽  
Improved Performance ◽  
Parallel Feature ◽  
Automated Technique

In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomography (CT), and so on, that can be analyzed by artificial intelligence methods for early diagnosis of diseases. Recently, the outbreak of the COVID-19 disease caused many deaths. Computer vision researchers support medical doctors by employing deep learning techniques on medical images to diagnose COVID-19 patients. Various methods were proposed for COVID-19 case classification. A new automated technique is proposed using parallel fusion and optimization of deep learning models. The proposed technique starts with a contrast enhancement using a combination of top-hat and Wiener filters. Two pre-trained deep learning models (AlexNet and VGG16) are employed and fine-tuned according to target classes (COVID-19 and healthy). Features are extracted and fused using a parallel fusion approach—parallel positive correlation. Optimal features are selected using the entropy-controlled firefly optimization method. The selected features are classified using machine learning classifiers such as multiclass support vector machine (MC-SVM). Experiments were carried out using the Radiopaedia database and achieved an accuracy of 98%. Moreover, a detailed analysis is conducted and shows the improved performance of the proposed scheme.

scholarly journals Pedestrian Detection under Parallel Feature Fusion Based on Choquet Integral

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 250
Author(s):  
Rong Yang ◽  
Yun Wang ◽  
Ying Xu ◽  
Li Qiu ◽  
Qiang Li
Keyword(s):  
Feature Fusion ◽  
Choquet Integral ◽  
Detection System ◽  
Pedestrian Detection ◽  
Texture Feature ◽  
Current Method ◽  
Detection Accuracy ◽  
Parallel Feature ◽  
New Feature ◽  
The Cost

Feature-based pedestrian detection method is currently the mainstream direction to solve the problem of pedestrian detection. In this kind of method, whether the appropriate feature can be extracted is the key to the comprehensive performance of the whole pedestrian detection system. It is believed that the appearance of a pedestrian can be better captured by the combination of edge/local shape feature and texture feature. In this field, the current method is to simply concatenate HOG (histogram of oriented gradient) features and LBP (local binary pattern) features extracted from an image to produce a new feature with large dimension. This kind of method achieves better performance at the cost of increasing the number of features. In this paper, Choquet integral based on the signed fuzzy measure is introduced to fuse HOG and LBP descriptors in parallel that is expected to improve accuracy without increasing feature dimensions. The parameters needed in the whole fusion process are optimized by a training algorithm based on genetic algorithm. This architecture has three advantages. Firstly, because the fusion of HOG and LBP features is parallel, the dimensions of the new features are not increased. Secondly, the speed of feature fusion is fast, thus reducing the time of pedestrian detection. Thirdly, the new features after fusion have the advantages of HOG and LBP features, which is helpful to improve the detection accuracy. The series of experimentation with the architecture proposed in this paper reaches promising and satisfactory results.

scholarly journals Comparison Analysis of Facebook’s Prophet, Amazon’s DeepAR+ and CNN-QR Algorithms for Successful Real-World Sales Forecasting

2021 ◽  
Vol 13 (2) ◽  
pp. 67-84
Author(s):  
Emir Žunić ◽  
Kemal Korjenić ◽  
Sead Delalić ◽  
Zlatko Šubara
Keyword(s):  
Time Series ◽  
Real World ◽  
Time Series Data ◽  
Real Data ◽  
Detailed Comparison ◽  
Series Data ◽  
Comparison Analysis ◽  
Forecasting Models ◽  
The Creation ◽  
Analysis Of Time Series

By successfully solving the problem of forecasting, the processes in the work of various companies are optimized and savings are achieved. In this process, the analysis of time series data is of particular importance. Since the creation of Facebook’s Prophet, and Amazon’s DeepAR+ and CNN-QR forecasting models, algorithms have attracted a great deal of attention. The paper presents the application and comparison of the above algorithms for sales forecasting in distribution companies. A detailed comparison of the performance of algorithms over real data with different lengths of sales history was made. The results show that Prophet gives better results for items with a longer history and frequent sales, while Amazon’s algorithms show superiority for items without a long history and items that are rarely sold.

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