scholarly journals Automatic Handwritten Devanagari Text Generation in Marathi Styles using Ant Miner Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 2541-2551
Keyword(s):  
Feature Extraction ◽  
Noise Removal ◽  
Text Generation ◽  
Indian Languages ◽  
Scale Invariant ◽  
Linear Discriminant ◽  
Document Recognition ◽  
Feature Extraction Method ◽  
Two Phases ◽  
Artificial Neural Network Ann

The Devanagari scripts forms the backbone of the writing system of several Indian languages includes Hindi, Sanskrit and Marathi. With the increased demand, exploration and globalization of digital Devanagari documents, different printed and handwritten document recognition techniques have involved since last two decades. In literature many methods of Devanagari script recognition have been used but it is not able to attain the best results in recognition. Hence, in this paper is proposed Ant Miner Algorithm (AMA) for recognition and text generation of handwritten Devanagari Marathi Scripts. The proposed method recognition process is working with the four different stages such as pre-processing, segmentation, feature extraction and recognition with text generation. The first stage pre-processing is consists of skew correction, noise removal and binarization. The second stage is segmentation that contains the line segmentation, word segmentation and character segmentation. The third stage is feature extraction method it contains four methods such as Scale Invariant Feature Transform (SIFT), Linear Discriminant Analysis (LDA), Discrete Cosine Transform (DCT) and Local Binary Pattern (LBP). The final stage is recognition and text generation with attain with the help of AMA algorithm. It works based on the two phases such as training and testing phase. The proposed method is implemented in the python platform and it compared with the Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN). The performance of the proposed method is analysed with statistical measurements of accuracy, precision and recall.

scholarly journals A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1631 ◽  
Author(s):  
Dong-Wei Chen ◽  
Rui Miao ◽  
Wei-Qi Yang ◽  
Yong Liang ◽  
Hao-Heng Chen ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Time Complexity ◽  
Extraction Method ◽  
Differential Entropy ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Original Dataset

Feature extraction of electroencephalography (EEG) signals plays a significant role in the wearable computing field. Due to the practical applications of EEG emotion calculation, researchers often use edge calculation to reduce data transmission times, however, as EEG involves a large amount of data, determining how to effectively extract features and reduce the amount of calculation is still the focus of abundant research. Researchers have proposed many EEG feature extraction methods. However, these methods have problems such as high time complexity and insufficient precision. The main purpose of this paper is to introduce an innovative method for obtaining reliable distinguishing features from EEG signals. This feature extraction method combines differential entropy with Linear Discriminant Analysis (LDA) that can be applied in feature extraction of emotional EEG signals. We use a three-category sentiment EEG dataset to conduct experiments. The experimental results show that the proposed feature extraction method can significantly improve the performance of the EEG classification: Compared with the result of the original dataset, the average accuracy increases by 68%, which is 7% higher than the result obtained when only using differential entropy in feature extraction. The total execution time shows that the proposed method has a lower time complexity.

An Improved Linear Discriminant Analysis Method and its Application to Face Recognition

2014 ◽  
Vol 556-562 ◽  
pp. 4825-4829 ◽  
Author(s):  
Kai Li ◽  
Peng Tang
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Extraction Method ◽  
Analysis Method ◽  
Scatter Matrix ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Discriminant Analysis Method

Linear discriminant analysis (LDA) is an important feature extraction method. This paper proposes an improved linear discriminant analysis method, which redefines the within-class scatter matrix and introduces the normalized parameter to control the bias and variance of eigenvalues. In addition, it makes the between-class scatter matrix to weight and avoids the overlapping of neighboring class samples. Some experiments for the improved algorithm presented by us are performed on the ORL, FERET and YALE face databases, and it is compared with other commonly used methods. Experimental results show that the proposed algorithm is the effective.

scholarly journals Analysis of classification methods suitable for band limited spatially filtered EEG signal applicable to non-invasive BCI

2019 ◽  
Vol 8 (9) ◽  
pp. 1689-1694
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Common Spatial Pattern ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Non Invasive ◽  
Band Limited ◽  
Ca Sensitivity

Electroencephalographic (EEG) signals are the preferred input for non-invasive Brain-Computer Interface (BCI). Efficient signal processing strategies, including feature extraction and classification, are required to distinguish the underlying task of BCI. This work proposes the optimized common spatial pattern(CSP) filtering technique as the feature extraction method for collecting the spatially spread variation of the signal. The bandpass filter (BPF) designed for this work assures the availability of event-related synchronized (ERS) and event-related desynchronized (ERD) signal as input to the spatial filter. This work takes consideration of the area-specific electrodes for feature formation. This work further proposes a comparative analysis of classifier algorithms for classification accuracy(CA), sensitivity and specificity and the considered algorithms are Support Vector Machine(SVM), Linear Discriminant Analysis(LDA), and K-Nearest Neighbor(KNN). Performance parameters considered are CA, sensitivity, and selectivity, which can judge the method not only for high CA but also inclining towards the particular class. Thus it will direct in the selection of appropriate classifier as well as tuning the classifier to get the balanced results. In this work, CA, the prior performance parameter is obtained to be 88.2% sensitivity of 94.2% and selectivity 82.2% for the cosine KNN classifier. SVM with linear kernel function also gives the comparable results, thus concluding that the robust classifiers perform well for all parameters in case of CSP for feature extraction.

Wavelets-Based Feature Extraction for Texture Classification

2010 ◽  
Vol 97-101 ◽  
pp. 1273-1276 ◽  
Author(s):  
Gang Yu ◽  
Ying Zi Lin ◽  
Sagar Kamarthi
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Energy Content ◽  
Texture Classification ◽  
Wavelet Coefficient ◽  
Discrimination Performance ◽  
Extraction Methods ◽  
Rotation Invariant ◽  
Scale Invariant ◽  
Feature Extraction Method

Texture classification is a necessary task in a wider variety of application areas such as manufacturing, textiles, and medicine. In this paper, we propose a novel wavelet-based feature extraction method for robust, scale invariant and rotation invariant texture classification. The method divides the 2-D wavelet coefficient matrices into 2-D clusters and then computes features from the energies inherent in these clusters. The features that contain the information effective for classifying texture images are computed from the energy content of the clusters, and these feature vectors are input to a neural network for texture classification. The results show that the discrimination performance obtained with the proposed cluster-based feature extraction method is superior to that obtained using conventional feature extraction methods, and robust to the rotation and scale invariant texture classification.

NEAREST NEIGHBOR DISCRIMINANT ANALYSIS

2006 ◽  
Vol 20 (08) ◽  
pp. 1245-1259 ◽  
Author(s):  
XIPENG QIU ◽  
LIDE WU
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
Small Sample Size ◽  
Small Sample ◽  
Real World Data ◽  
Scatter Matrix ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Gaussian Density

Linear Discriminant Analysis (LDA) is a popular feature extraction technique in statistical pattern recognition. However, it often suffers from the small sample size problem when dealing with high-dimensional data. Moreover, while LDA is guaranteed to find the best directions when each class has a Gaussian density with a common covariance matrix, it can fail if the class densities are more general. In this paper, a novel nonparametric linear feature extraction method, nearest neighbor discriminant analysis (NNDA), is proposed from the view of the nearest neighbor classification. NNDA finds the important discriminant directions without assuming the class densities belong to any particular parametric family. It does not depend on the nonsingularity of the within-class scatter matrix either. Then we give an approximate approach to optimize NNDA and an extension to k-NN. We apply NNDA to the simulated data and real world data, the results demonstrate that NNDA outperforms the existing variant LDA methods.

An Algorithm of Frequent Neighboring Class Set Mining with Constraint Class

2011 ◽  
Vol 211-212 ◽  
pp. 808-812
Author(s):  
Gang Fang ◽  
Hong Ying ◽  
Jiang Xiong ◽  
Yuan Bin Wu
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Dimensional Space ◽  
Recognition Rate ◽  
Low Frequency ◽  
Expression Change ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Low Dimensional ◽  
The Impact

In this paper, the purpose is to find a method that can be more suited to facial expression change and also improve the recognition rate. The proposed system contains three parts, wavelet transform, Fisher linear discriminant method feature extraction and face classification. The basic idea of the proposed method is that first extract the low-frequency components through wavelet transform, then the low-frequency images mapped into a low-dimensional space by PCA transform, and finally the utilization of LDA feature extraction method in low-dimensional space. The algorithms were tested on ORL and Yale face database, respectively. Experimental results shows that the proposed method not only improve the recognition rate, but also improve the recognition speed. This method can effectively overcome the impact of expression changes on face recognition, and play a certain role in inhibition of expression.

FEATURE EXTRACTION OF MOTOR IMAGERY EEG BASED ON WAVELET TRANSFORM AND HIGHER-ORDER STATISTICS

2010 ◽  
Vol 08 (03) ◽  
pp. 373-384 ◽  
Author(s):  
RENHUAN YANG ◽  
AIGUO SONG ◽  
BAOGUO XU
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Order Statistics ◽  
Motor Imagery ◽  
Higher Order ◽  
Higher Order Statistics ◽  
Data Set ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Harmonic Wavelet

Feature extraction plays an important role in brain-computer interface (BCI) systems. In order to characterize the motor imagery related rhythm and higher-order statistics information contained within the EEG signals, a novel feature extraction method based on harmonic wavelet transform and bispectrum is developed and applied to the recognition of right and left motor imageries for developing EEG-based BCI systems. The experimental results on the Graz BCI data set have shown that the separability of the two classes features extracted by the proposed method is notable. Its performance was evaluated by a linear discriminant analysis (LDA) classifier. The recognition accuracy of 90% was obtained. The recognition results have demonstrated the effectiveness of the proposed method. This method provides an effective way for EEG feature extraction in BCI system.

scholarly journals Coal and Gas Outbursts Prediction Based on DWT+FICA-LDA Feature Extraction and QPSO-DELM Classifier

2020 ◽  
Author(s):  
Xuning Liu ◽  
Zhixiang Li ◽  
Zixian Zhang ◽  
Guoying Zhang
Keyword(s):  
Feature Extraction ◽  
Processing Technique ◽  
Discrete Wavelet ◽  
Data Set ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Fast Independent Component Analysis ◽  
Coal And Gas Outbursts ◽  
Learning Machine ◽  
High Order Statistics

Abstract Due to the severity and great harm of coal and gas outbursts accidents, outbursts prediction becomes very necessary, the paper presents a hybrid prediction model of feature extraction and pattern classification for coal and gas outbursts. First, Discrete wavelet transform (DWT) is utilized as a processing technique to decompose subseries and extract the features with different frequency, and the optimal feature components are retained; Second, in order to eliminate the redundancy between the features and uncorrelation between feature and outbursts, we use the fast independent component analysis(FICA) feature extraction method based on high-order statistics to obtain each independent feature, obtaining the global information in the feature; then the obtained features are input into linear discriminant analysis(LDA) , under the guidance of class label, then the local information in features are obtained; Finally, the projected features are input into the deep extreme learning machine(DELM) classifier based on the optimal parameters by quantum particle swarm optimization(QPSO) for training and classification. The experimental results on the data set of coal and gas outbursts show that compared with other models in the current prediction of coal and gas outbursts, this method has significant effect on various indicators such as speed and recognition effect.

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