Performance analysis of SVM with quadratic kernel and logistic regression in classification of wild animals

Suhas, M V (2019) Performance analysis of SVM with quadratic kernel and logistic regression in classification of wild animals. An Internatioanl Journal of Advanced Computer Technology.

[img] PDF
7846.pdf
Restricted to Registered users only

Download (201kB) | Request a copy

Abstract

In an attempt to develop a system to classify the wild animals using image processing and classification techniques, we study the usage of Haralick textural features are used in wild animal classification which is a computer aided pattern recognition system. The Haralick features from two wild animal classes that include leopard and wildcat are extracted to from the image database. Support Vector Machine (SVM) with quadratic kernel function model and Logistic Regression (LR) model are developed and tested using the created dataset. In each case, the performance of the classifier is measured.We also compare the performances of SVM and LR with and without pre-processing the dataset using Principal Component Analysis (PCA). This study reveals an increment in the accuracy post pre-processing of the dataset

Item Type: Article
Subjects: Engineering > MIT Manipal > Electronics and Communication
Depositing User: MIT Library
Date Deposited: 23 Jun 2020 05:36
Last Modified: 23 Jun 2020 05:36
URI: http://eprints.manipal.edu/id/eprint/155138

Actions (login required)

View Item View Item