scholarly journals Comparison of Feature Extraction Techniques for Pattern Classification

2021 ◽  
Vol 9 (VI) ◽  
pp. 5511-5517
Author(s):  
Binu P Chacko
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Research Field ◽  
Svm Classifier ◽  
Discriminative Power ◽  
Extraction Techniques ◽  
Pattern Recognition Problem ◽  
Division Point ◽  
Research Problems

Pattern recognition is a challenging task in research field for the last few decades. Many researchers have worked on areas such as computer vision, speech recognition, document classification, and computational biology to tackle complex research problems. In this article, a pattern recognition problem for handwritten Malayalam character is presented. This system goes through two different stages of HCR namely, feature extraction and classification. Three feature extraction techniques – wavelet transform, zoning, division point – are used in this study. Among these, division is point is able to show best discriminative power using SVM classifier. All the experiments are conducted on size normalized and binarized images of isolated Malayalam characters.

scholarly journals Brahmi Word Recognition by Supervised Techniques

2020 ◽  
Author(s):  
Neha Gautam ◽  
Soo See Chai ◽  
Sadia Afrin ◽  
Jais Jose
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Complex Structure ◽  
Support Vector ◽  
Svm Classifier ◽  
Pattern Recognition Problem ◽  
Word Level ◽  
Single Feature ◽  
Better Than ◽  
Made In

Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the recognition of characters, compound characters, and word because of complex structure. For this kind of complex pattern recognition problem, it is always difficult to decide which feature extraction and classifier would be the best choice. Moreover, it is also true that different feature extraction and classifiers offer complementary information about the patterns to be classified. Therefore, combining feature extraction and classifiers, in an intelligent way, can be beneficial compared to using any single feature extraction. This study proposed the combination of HOG +zonal density with SVM to recognize the Brahmi words. Keeping these facts in mind, in this paper, information provided by structural and statistical based features are combined using SVM classifier for script recognition (word-level) purpose from the Brahmi words images. Brahmi word dataset contains 6,475 and 536 images of Brahmi words of 170 classes for the training and testing, respectively, and the database is made freely available. The word samples from the mentioned database are classified based on the confidence scores provided by support vector machine (SVM) classifier while HOG and zonal density use to extract the features of Brahmi words. Maximum accuracy suggested by system is 95.17% which is better than previously suggested studies.

scholarly journals Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 114
Author(s):  
Tiziano Zarra ◽  
Mark Gino K. Galang ◽  
Florencio C. Ballesteros ◽  
Vincenzo Belgiorno ◽  
Vincenzo Naddeo
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Performance Optimization ◽  
Industrial Plant ◽  
Mathematical Treatment ◽  
Extraction Techniques ◽  
Peak Period ◽  
Linear Discriminant ◽  
Artificial Neural

Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the acquired data, and the validation of the correlation of the odour metric are key topics to control in order to ensure a robust and reliable measurement. The research presents and discusses the use of different pattern recognition and feature extraction techniques in the elaboration and effectiveness of the odour classification monitoring model (OCMM). The effect of the rise, intermediate, and peak period from the original response curve, in collaboration with Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) as a pattern recognition algorithm, were investigated. Laboratory analyses were performed with real odour samples collected in a complex industrial plant, using an advanced smart IOMS. The results demonstrate the influence of the choice of method on the quality of the OCMM produced. The peak period in combination with the Artificial Neural Network (ANN) highlighted the best combination on the basis of high classification rates. The paper provides information to develop a solution to optimize the performance of IOMS.

Recognition of Vehicles on Geometric Morphology

2011 ◽  
Vol 217-218 ◽  
pp. 27-32
Author(s):  
Guo Feng Qin ◽  
Yu Sun ◽  
Qi Yan Li
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Traffic Management ◽  
Template Matching ◽  
Recognition System ◽  
Automatic Recognition ◽  
Vehicular Traffic ◽  
Image Pretreatment ◽  
Geometric Morphology

Detection of vehicles plays an important role in the area of the modern intelligent traffic management. And the pattern recognition is a hot issue in the area of computer vision. This article introduces an Automobile Automatic Recognition System based on image. It begins with the structures of the system. Then detailed methods for implementation are discussed. This system take use of a camera to get traffic images, then after image pretreatment and segmentation, do the works of feature extraction, template matching and pattern recognition, to identify different models and get vehicular traffic statistics. Finally, the implementation of the system is introduced. The algorithms of recognized process were verified in this application case.

Analysis of Plant Disease Detection and Classification Models: A Computer Vision Perspective

2020 ◽  
Vol 17 (12) ◽  
pp. 5422-5428
Author(s):  
K. Jayaprakash ◽  
S. P. Balamurugan
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Plant Disease ◽  
Weather Conditions ◽  
Vital Role ◽  
Plant Diseases ◽  
Disease Detection ◽  
Classification Models ◽  
Extraction Techniques ◽  
Identification Process

Presently, rapid and precise disease identification process plays a vital role to increase agricultural productivity in a sustainable manner. Conventionally, human experts identify the existence of anomaly in plants occurred due to disease, pest, nutrient deficient, weather conditions. Since manual diagnosis process is a tedious and time consuming task, computer vision approaches have begun to automatically detect and classify the plant diseases. The general image processing tasks involved in plant disease detection are preprocessing, segmentation, feature extraction and classification. This paper performs a review of computer vision based plant disease detection and classification techniques. The existing plant disease detection approaches including segmentation and feature extraction techniques have been reviewed. Additionally, a brief survey of machine learning (ML) and deep learning (DL) models to identify plant diseases also takes place. Furthermore, a set of recently developed DL based tomato plant leaf disease detection and classification models are surveyed under diverse aspects. To further understand the reviewed methodologies, a detailed comparative study also takes place to recognize the unique characteristics of the reviewed models.

An Efficient and Robust Technique for Facial Expression Recognition Using Modified Hidden Markov Model

2018 ◽  
Vol 9 (3) ◽  
pp. 12-22
Author(s):  
Mayur Rahul ◽  
Pushpa Mamoria ◽  
Narendra Kohli ◽  
Rashi Agrawal
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Hidden Markov ◽  
Bottom Layer ◽  
Expression Recognition ◽  
Multi Stage ◽  
Better Than

Partition-based feature extraction is widely used in the pattern recognition and computer vision. This method is robust to some changes like occlusion, background, etc. In this article, a partition-based technique is used for feature extraction and extension of HMM is used as a classifier. The new introduced multi-stage HMM consists of two layers. In which bottom layer represents the atomic expression made by eyes, nose and lips. Further, the upper layer represents the combination of these atomic expressions such as smile, fear, etc. Six basic facial expressions are recognized, i.e. anger, disgust, fear, joy, sadness and surprise. Experimental results show that the proposed system performs better than normal HMM and has an overall accuracy of 85% using the JAFFE database.

Devanagari Handwritten Marathi Compound Character Recognition System

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Chitra Bhole
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Character Recognition ◽  
Wavelet Transformation ◽  
Recognition System ◽  
Computational Time ◽  
Deep Convolution Neural Network ◽  
Time Required

Handwritten character recognition a field of research in AI, computer vision, and pattern recognition. Devanagari handwritten Marathi compound character recognition is most tedious tasks because of its complexity as compared to other languages. As compound character is combination of two or more characters it becomes challenging task to recognize it. However, the researchers used various methods like Neural Network, SVM, KNN, Wavelet transformation to classify the features of compound Marathi characters and tried to give the accuracy in the recognition of it. But the problem of feature extraction, and time required is large. In this paper I am proposing the Offline handwritten Marathi compound character recognition using deep convolution neural network which reduces the computational time and increases the accuracy.

ADAPTIVE KERNEL DISCRIMINANT ANALYSIS AND ITS APPLICATIONS ON PATTERN RECOGNITION

2006 ◽  
Vol 03 (04) ◽  
pp. 329-337
Author(s):  
JUN-BAO LI ◽  
JENG-SHYANG PAN
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Discriminant Analysis ◽  
Pattern Classification ◽  
Input Data ◽  
Feature Space ◽  
Extraction Techniques ◽  
Kernel Discriminant Analysis ◽  
Adaptive Kernel ◽  
Discriminatory Information

In pattern recognition, feature extraction techniques are widely employed to reduce the dimensionality of data and to enhance the discriminatory information. In this paper, we present an extension of KFD method based on the data-dependent kernel, called the adaptive kernel discriminant analysis (AKDA), for feature extraction and pattern classification. AKDA is more adaptive to the input data than KDA owing to the optimization of projection from input space to feature space with the data-dependent kernel, which enhances the performance of KDA. Experimental results on ORL, Yale and MNIST database show that the proposed AKDA gives the higher performance than KDA.

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