scholarly journals Research on the Application of Wireless Wearable Sensing Devices in Interactive Music

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Huizhong Wang
Keyword(s):  
Recognition Rate ◽  
Principal Component ◽  
Feature Space ◽  
Wearable Devices ◽  
Subjective Feeling ◽  
Continuous Variables ◽  
Fusion Algorithm ◽  
Interactive Music ◽  
Experimental Scheme ◽  
Emotional Model

Wireless wearable devices can greatly assist and promote the artistic presentation of interactive music and have attracted the attention of more and more composers, musicians, dancers, and visual artists. It can pick up data information in real time, integrate with performers, and provide immersive performance experience. It not only builds a bridge between subjective feeling and spiritual perception for performers and audience but also enables audience to directly observe art information better. This paper mainly introduces the development process of a wearable sensor system designed for monitoring interactive music movement. Firstly, an interactive music motion model is established according to the principle of human body kinematics, and the experimental scheme of measuring the swaying angle of interactive music with a single sensor device is standardized. A multisensor fusion algorithm is proposed to estimate the swing angle of interactive music. Based on the “cost-incentive” emotional model, the wireless wearable device and interactive music model are regarded as continuous variables determined by the emotional effect value and the incentive value. Extract energy, rhythm, harmony, time domain, and spectrum features of interactive music of wireless wearable devices, and reduce the dimension of a music feature space through principal component analysis, spatial projection, and relief feature selection. Finally, the practicability of the system and the accuracy of the algorithm are verified by experiments. The recognition rate of wireless wearable devices and interactive music realized based on this algorithm was improved.

Face Recognition by PCA and Improved LBP Fusion Algorithm

2015 ◽  
Vol 734 ◽  
pp. 562-567 ◽  
Author(s):  
En Zeng Dong ◽  
Yan Hong Fu ◽  
Ji Gang Tong
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Principal Component ◽  
Texture Features ◽  
Rotation Invariant ◽  
Fusion Algorithm ◽  
Face Images ◽  
The Face ◽  
Pca Method

This paper proposed a theoretically efficient approach for face recognition based on principal component analysis (PCA) and rotation invariant uniform local binary pattern texture features in order to weaken the effects of varying illumination conditions and facial expressions. Firstly, the rotation invariant uniform LBP operator was adopted to extract the local texture feature of the face images. Then PCA method was used to reduce the dimensionality of the extracted feature and get the eigenfaces. Finally, the nearest distance classification was used to distinguish each face. The method has been accessed on Yale and ATR-Jaffe face databases. Results demonstrate that the proposed method is superior to standard PCA and its recognition rate is higher than the traditional PCA. And the proposed algorithm has strong robustness against the illumination changes, pose, rotation and expressions.

Anatomization of the systems of dimension relaxation for facial recognition

2020 ◽  
pp. 1-11
Author(s):  
Mayamin Hamid Raha ◽  
Tonmoay Deb ◽  
Mahieyin Rahmun ◽  
Tim Chen
Keyword(s):  
Face Recognition ◽  
Main Idea ◽  
Principal Component ◽  
Feature Space ◽  
Projection Methods ◽  
Data Representation ◽  
Linear Discriminant ◽  
Class Differences ◽  
Underlying Space ◽  
Maximum Variance

Face recognition is the most efficient image analysis application, and the reduction of dimensionality is an essential requirement. The curse of dimensionality occurs with the increase in dimensionality, the sample density decreases exponentially. Dimensionality Reduction is the process of taking into account the dimensionality of the feature space by obtaining a set of principal features. The purpose of this manuscript is to demonstrate a comparative study of Principal Component Analysis and Linear Discriminant Analysis methods which are two of the highly popular appearance-based face recognition projection methods. PCA creates a flat dimensional data representation that describes as much data variance as possible, while LDA finds the vectors that best discriminate between classes in the underlying space. The main idea of PCA is to transform high dimensional input space into the function space that displays the maximum variance. Traditional LDA feature selection is obtained by maximizing class differences and minimizing class distance.

scholarly journals Parameters Optimization and Application of SVM Based on PCA-Particle Swarm Algorithm

2019 ◽  
pp. 325-330 ◽  
Author(s):  
Qingmi Yang
Keyword(s):  
Parameter Optimization ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Principal Component ◽  
Pso Algorithm ◽  
Parameters Optimization ◽  
Support Vector ◽  
Particle Swarm Algorithm

The parameter optimization of Support Vector Machine (SVM) has been a hot research direction. To improve the optimization rate and classification performance of SVM, the Principal Component Analysis (PCA) - Particle Swarm Optimization (PSO) algorithm was used to optimize the penalty parameters and kernel parameters of SVM. PSO which is to find the optimal solution through continuous iteration combined with PCA that eliminates linear redundancy between data, effectively enhance the generalization ability of the model, reduce the optimization time of parameters, and improve the recognition accuracy. The simulation comparison experiments on 6 UCI datasets illustrate that the excellent performance of the PCA-PSO-SVM model. The results show that the proposed algorithm has higher recognition accuracy and better recognition rate than simple PSO algorithm in the parameter optimization of SVM. It is an effective parameter optimization method.

Soil sampling design optimization by using conditioned Latin Hypercube sampling    

2021 ◽  
Author(s):  
Fatemeh Hateffard ◽  
Tibor József Novák
Keyword(s):  
Principal Component ◽  
Latin Hypercube Sampling ◽  
Feature Space ◽  
Simple Random Sampling ◽  
Soil Sampling ◽  
Latin Hypercube ◽  
Landsat Images ◽  
Original Distribution ◽  
Spatial Coverage ◽  
Elevation Model

<p>One of the most critical steps in digital soil mapping is finding a sampling approach to cover a good spatial coverage of the area regarding the soil spatial variation. In this matter, environmental variables can aid in taking samples in more innovative and more precise locations while reducing the soil sampling efforts such as time and costs. Conditioned Latin hypercube sampling (cLHS) is a stratified random design strategy that perfectly represents the variability of auxiliary variables in feature space. This study applied this method and compared it to simple random sampling to optimize sampling designs for mapping in the agricultural study site in Hungary. The covariates were indices extracted by the digital elevation model and Landsat images. The principal component analysis (PCA) was applied to reduce the data overlap and select the most important variables as the model's inputs. By computing the statistical criteria (mean, variance, standard deviation, etc.) for covariates and comparing these results between the sampling populations and the entire one, we may conclude that both designs gave almost similar predictions. However, for most covariates, statistical means of cLHS provide the closest approximation compared to the random approach sampling method, but the statistical variances and SDs retrieved similar results. Furthermore, the histogram distribution of most variables in the cLHS was following more closely to the original distribution of the environmental covariates. Overall, considering the type of the study site and the chosen variables, it seems that cLHS is a more applicable method.</p> <p> </p>

Learning Target Class Feature Subspace (LTC-FS) Using Eigenspace Analysis and N-ary Search-Based Autonomous Hyperparameter Tuning for OCSVM

2021 ◽  
Author(s):  
Sanjay Kumar Sonbhadra ◽  
Sonali Agarwal ◽  
P. Nagabhushan
Keyword(s):  
Principal Component ◽  
Feature Space ◽  
Support Vector ◽  
Feature Subset ◽  
Target Class ◽  
Significant Information ◽  
Feature Extraction Method ◽  
Specificity And Sensitivity ◽  
Feature Subspace ◽  
Novel Target

Existing dimensionality reduction (DR) techniques such as principal component analysis (PCA) and its variants are not suitable for target class mining due to the negligence of unique statistical properties of class-of-interest (CoI) samples. Conventionally, these approaches utilize higher or lower eigenvalued principal components (PCs) for data transformation; but the higher eigenvalued PCs may split the target class, whereas lower eigenvalued PCs do not contribute significant information and wrong selection of PCs leads to performance degradation. Considering these facts, the present research offers a novel target class-guided feature extraction method. In this approach, initially, the eigendecomposition is performed on variance–covariance matrix of only the target class samples, where the higher- and lower-valued eigenvectors are rejected via statistical analysis, and the selected eigenvectors are utilized to extract the most promising feature subspace. The extracted feature-subset gives a more tighter description of the CoI with enhanced associativity among target class samples and ensures the strong separation from nontarget class samples. One-class support vector machine (OCSVM) is evaluated to validate the performance of learned features. To obtain optimized values of hyperparameters of OCSVM a novel [Formula: see text]-ary search-based autonomous method is also proposed. Exhaustive experiments with a wide variety of datasets are performed in feature-space (original and reduced) and eigenspace (obtained from original and reduced features) to validate the performance of the proposed approach in terms of accuracy, precision, specificity and sensitivity.

Medical Image Fusion in Wavelet and Ridgelet Domains

2017 ◽  
pp. 711-723
Author(s):  
Vikrant Bhateja ◽  
Abhinav Krishn ◽  
Himanshi Patel ◽  
Akanksha Sahu
Keyword(s):  
Image Fusion ◽  
Information Content ◽  
Medical Image ◽  
Similarity Index ◽  
Principal Component ◽  
Structural Similarity ◽  
Fusion Algorithm ◽  
Medical Image Fusion ◽  
Multi Scale ◽  
Fused Image

Medical image fusion facilitates the retrieval of complementary information from medical images and has been employed diversely for computer-aided diagnosis of life threatening diseases. Fusion has been performed using various approaches such as Pyramidal, Multi-resolution, multi-scale etc. Each and every approach of fusion depicts only a particular feature (i.e. the information content or the structural properties of an image). Therefore, this paper presents a comparative analysis and evaluation of multi-modal medical image fusion methodologies employing wavelet as a multi-resolution approach and ridgelet as a multi-scale approach. The current work tends to highlight upon the utility of these approaches according to the requirement of features in the fused image. Principal Component Analysis (PCA) based fusion algorithm has been employed in both ridgelet and wavelet domains for purpose of minimisation of redundancies. Simulations have been performed for different sets of MR and CT-scan images taken from ‘The Whole Brain Atlas'. The performance evaluation has been carried out using different parameters of image quality evaluation like: Entropy (E), Fusion Factor (FF), Structural Similarity Index (SSIM) and Edge Strength (QFAB). The outcome of this analysis highlights the trade-off between the retrieval of information content and the morphological details in finally fused image in wavelet and ridgelet domains.

A Hybrid Approach for Facial Expression Recognition Using Extended Local Binary Patterns and Principal Component Analysis

2019 ◽  
Vol 8 (2) ◽  
pp. 1-25
Author(s):  
Gopal Krishan Prajapat ◽  
Rakesh Kumar
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Local Binary Pattern ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Expression Recognition ◽  
Distance Method

Facial feature extraction and recognition plays a prominent role in human non-verbal interaction and it is one of the crucial factors among pose, speech, facial expression, behaviour and actions which are used in conveying information about the intentions and emotions of a human being. In this article an extended local binary pattern is used for the feature extraction process and a principal component analysis (PCA) is used for dimensionality reduction. The projections of the sample and model images are calculated and compared by Euclidean distance method. The combination of extended local binary pattern and PCA (ELBP+PCA) improves the accuracy of the recognition rate and also diminishes the evaluation complexity. The evaluation of proposed facial expression recognition approach will focus on the performance of the recognition rate. A series of tests are performed for the validation of algorithms and to compare the accuracy of the methods on the JAFFE, Extended Cohn-Kanade images database.

scholarly journals Improving Human Motion Classification by Applying Bagging and Symmetry to PCA-Based Features

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1264 ◽  
Author(s):  
Tomasz Hachaj
Keyword(s):  
Nearest Neighbor ◽  
Recognition Rate ◽  
Principal Component ◽  
Human Motion ◽  
Support Vector ◽  
Human Observer ◽  
Weak Classifier ◽  
Motion Classification ◽  
Observer Motion ◽  
Human Motion Classification

This paper proposes a method for improving human motion classification by applying bagging and symmetry to Principal Component Analysis (PCA)-based features. In contrast to well-known bagging algorithms such as random forest, the proposed method recalculates the motion features for each “weak classifier” (it does not randomly sample a feature set). The proposed classification method was evaluated on a challenging (even to a human observer) motion capture recording dataset of martial arts techniques performed by professional karate sportspeople. The dataset consisted of 360 recordings in 12 motion classes. Because some classes of these motions might be symmetrical (which means that they are performed with a dominant left or right hand/leg), an analysis was conducted to determine whether accounting for symmetry could improve the recognition rate of a classifier. The experimental results show that applying the proposed classifiers’ bagging procedure increased the recognition rate (RR) of the Nearest-Neighbor (NNg) and Support Vector Machine (SVM) classifiers by more than 5% and 3%, respectively. The RR of one trained classifier (SVM) was higher when we did not use symmetry. On the other hand, the application of symmetry information for bagged NNg improved its recognition rate compared with the results without symmetry information. We can conclude that symmetry information might be helpful in situations in which it is not possible to optimize the decision borders of the classifier (for example, when we do not have direct information about class labels). The experiment presented in this paper shows that, in this case, bagging and mirroring might help find a similar object in the training set that shares the same class label. Both the dataset that was used for the evaluation and the implementation of the proposed method can be downloaded, so the experiment is easily reproducible.

Out-of-sample data visualization using bi-kernel t-SNE

2020 ◽  
pp. 147387162097820
Author(s):  
Haili Zhang ◽  
Pu Wang ◽  
Xuejin Gao ◽  
Yongsheng Qi ◽  
Huihui Gao
Keyword(s):  
Data Visualization ◽  
Principal Component ◽  
Feature Space ◽  
Gaussian Kernel ◽  
Sample Data ◽  
Out Of Sample ◽  
Benchmark Datasets ◽  
The Difference ◽  
Low Dimensional ◽  
Effective Visualization

T-distributed stochastic neighbor embedding (t-SNE) is an effective visualization method. However, it is non-parametric and cannot be applied to steaming data or online scenarios. Although kernel t-SNE provides an explicit projection from a high-dimensional data space to a low-dimensional feature space, some outliers are not well projected. In this paper, bi-kernel t-SNE is proposed for out-of-sample data visualization. Gaussian kernel matrices of the input and feature spaces are used to approximate the explicit projection. Then principal component analysis is applied to reduce the dimensionality of the feature kernel matrix. Thus, the difference between inliers and outliers is revealed. And any new sample can be well mapped. The performance of the proposed method for out-of-sample projection is tested on several benchmark datasets by comparing it with other state-of-the-art algorithms.

Low-Resolution Face Recognition of Multi-Scale Blocking CS-LBP and Weighted PCA

2016 ◽  
Vol 30 (08) ◽  
pp. 1656005 ◽  
Author(s):  
Jiadi Li ◽  
Zhenxue Chen ◽  
Chengyun Liu
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Principal Component ◽  
Low Resolution ◽  
Multi Scale ◽  
Face Features ◽  
Weighted Principal Component ◽  
Pca Algorithm

A novel method is proposed in this paper to improve the recognition accuracy of Local Binary Pattern (LBP) on low-resolution face recognition. More precise descriptors and effectively face features can be extracted by combining multi-scale blocking center symmetric local binary pattern (CS-LBP) based on Gaussian pyramids and weighted principal component analysis (PCA) on low-resolution condition. Firstly, the features statistical histograms of face images are calculated by multi-scale blocking CS-LBP operator. Secondly, the stronger classification and lower dimension features can be got by applying weighted PCA algorithm. Finally, the different classifiers are used to select the optimal classification categories of low-resolution face set and calculate the recognition rate. The results in the ORL human face databases show that recognition rate can get 89.38% when the resolution of face image drops to 12[Formula: see text]10 pixel and basically satisfy the practical requirements of recognition. The further comparison of other descriptors and experiments from videos proved that the novel algorithm can improve recognition accuracy.

Sign in / Sign up

Export Citation Format

Share Document