Derivation, optimization, and comparative analysis of support vector machines application to multi-class image data

Avi Shekhar; Amir K. Saeed; Benjamin A. Johnson; Benjamin M. Rodriguez

doi:10.1117/12.3014060

7 June 2024 Derivation, optimization, and comparative analysis of support vector machines application to multi-class image data

Avi Shekhar, Amir K. Saeed, Benjamin A. Johnson, Benjamin M. Rodriguez

Author Affiliations +

Proceedings Volume 13033, Multimodal Image Exploitation and Learning 2024; 1303308 (2024) https://doi.org/10.1117/12.3014060
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Support Vector Machines (SVM) have emerged as a powerful and versatile machine learning technique for solving classification and regression problems. This paper presents a thorough review of SVM, encompassing its motivation, derivation of the optimization problem, the utilization of kernels for data transformation, and a comprehensive analysis of solution methods. The review is supported by experiments conducted on a data set derived from the Traffic Sign data set. The motivation for SVM lies in its ability to address complex classification tasks by transforming the data into a higher-dimensional feature space. This is particularly beneficial for data sets derived from multiple sources. The findings presented in this paper contribute to a better understanding of SVM’s capabilities.

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

Citation Download Citation

Avi Shekhar, Amir K. Saeed, Benjamin A. Johnson, and Benjamin M. Rodriguez "Derivation, optimization, and comparative analysis of support vector machines application to multi-class image data", Proc. SPIE 13033, Multimodal Image Exploitation and Learning 2024, 1303308 (7 June 2024); https://doi.org/10.1117/12.3014060

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