Comparative Analysis of Thermogram and Pre-Processed HoG Images Using Machine Learning Classifiers

Yuvaraj Munian; Antonio Martinez-Molina; Miltiadis Alamaniotis

doi:10.1109/IISA59645.2023.10345890

Back

Conference proceeding

Comparative Analysis of Thermogram and Pre-Processed HoG Images Using Machine Learning Classifiers

Yuvaraj Munian, Antonio Martinez-Molina and Miltiadis Alamaniotis

2023 14th International Conference on Information, Intelligence, Systems & Applications (IISA), pp 1-8

10 Jul 2023

DOI: https://doi.org/10.1109/IISA59645.2023.10345890

Additional Links

Abstract

Animals

Classification algorithms

DTA

Guinea-Bissau

histogram of oriented gradients (HOG)

Image processing

LDA

Logistic regression

Machine learning algorithms

machine learning classifiers-KNN

Prediction algorithms

Support vector machines

SVM

thermal images

Recently, machine learning models have been widely used to measure the performance of classification tasks. As per the specific applications, different classifiers are compared and summarized. In this paper, a set of thermal images is collected during nighttime hours and utilized for classification purposes. Machine learning models like Gaussian Naive Bayes, Decision Tree Algorithm, Random Forest, Linear Discriminant Analysis, Logistic Regression, Support Vector Machine, and K-Nearest Neighbor are used in this paper. Because of the complexity of the features in the thermal image, image processing is introduced to pre-processing before classification. The transformation of thermal images into HOG images is the conversion for reducing the intricacy of the thermal images. This paper exposes the optimal relative study between thermal and HOG images with the above types of machine learning classifiers. The most populated and spotted animal, deer, is the subject of this classification. The results gaudily conclude the classification of machine learning classifiers for thermal and HOG images with the highest accuracy of 90% for the random forest classifier.

Metrics

9 Record Views

1 citations in Scopus

Details

Title: Comparative Analysis of Thermogram and Pre-Processed HoG Images Using Machine Learning Classifiers
Creators: Yuvaraj Munian - The University of Texas at San Antonio
Antonio Martinez-Molina - Drexel University
Miltiadis Alamaniotis - The University of Texas at San Antonio
Publication Details: 2023 14th International Conference on Information, Intelligence, Systems & Applications (IISA), pp 1-8
Publisher: IEEE
Resource Type: Conference proceeding
Language: English
Academic Unit: Architecture, Design, and Urbanism
Scopus ID: 2-s2.0-85182022713
Other Identifier: 991021889910404721

Comparative Analysis of Thermogram and Pre-Processed HoG Images Using Machine Learning Classifiers

Additional Links

Abstract

Metrics

Details

Drexel University Social media