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

M. V. Suhas, B. P. Swathi

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)3069-3074
Number of pages6
JournalCompusoft
Volume8
Issue number2
Publication statusPublished - 01-01-2019
Externally publishedYes

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Support vector machines
Logistics
Animals
Pattern recognition systems
Image classification
Processing
Principal component analysis
Image processing
Classifiers

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Cite this

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Performance analysis of SVM with quadratic kernel and logistic regression in classification of wild animals. / Suhas, M. V.; Swathi, B. P.

In: Compusoft, Vol. 8, No. 2, 01.01.2019, p. 3069-3074.

Research output: Contribution to journalArticle

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