Optimized image classification techniques: comparing orthogonal basis and logistic regression with entropy and PCA in cat and dog image recognition

Ruixing Lu; Zhihan Cheng; Kaige Zhou; Yue Wu

doi:10.1117/12.3050225

12 November 2024 Optimized image classification techniques: comparing orthogonal basis and logistic regression with entropy and PCA in cat and dog image recognition

Ruixing Lu, Zhihan Cheng, Kaige Zhou, Yue Wu

Author Affiliations +

Proceedings Volume 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) ; 133953U (2024) https://doi.org/10.1117/12.3050225
Event: International Conference on Optics, Electronics, and Communication Engineering, 2024, Wuhan, China

Abstract

This research paper provides a pragmatic basis to use quaternions in application to machine learning and assesses cat and dog image classification with a twist of using OBLR (Orthogonal Basis and Logistic Regression) with PCA (Principal Component Analysis) and Entropy. The OBLR method uses the Gram-Schmidt technique of orthogonalization to derive an independent, orthogonal feature space for cats and dogs separately, followed by application with logistic regression for categorization. In simple terms, “The Entropy and PCA Method” reduces the data dimensionality in the image with PCA and classifies based on the variation of entropy among classes. It picks the limitations of these two methods and chooses the advantages. The result of the study indicated that both the methods considered were found effective in the classification of images containing cats and dogs, and therefore, useful applications in the management of stray animals and control of pet health. Our forthcoming scopes will include the implementation of OBLR and PCA with entropy, the implementation of more sophisticated machine learning models like CNNs, and statistical metrics of feature selection techniques that would be useful to further improve the classification accuracy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ruixing Lu, Zhihan Cheng, Kaige Zhou, and Yue Wu "Optimized image classification techniques: comparing orthogonal basis and logistic regression with entropy and PCA in cat and dog image recognition", Proc. SPIE 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) , 133953U (12 November 2024); https://doi.org/10.1117/12.3050225

Access the abstract

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY