Combination of texture feature extraction and forward selection for one-class support vector machine improvement in self-portrait classification

Reina Alya Rahma (Lambung Mangkurat University)
Radityo Adi Nugroho (Lambung Mangkurat University)
Dwi Kartini (Lambung Mangkurat University)
Mohammad Reza Faisal (Lambung Mangkurat University)
Friska Abadi (Lambung Mangkurat University)

International Journal of Electrical and Computer Engineering
Vol 13, No 1: February 2023

This study aims to validate self-portraits using one-class support vector machine (OCSVM). To validate accurately, we build a model by combining texture feature extraction methods, Haralick and local binary pattern (LBP). We also reduce irrelevant features using forward selection (FS). OCSVM was selected because it can solve the problem caused by the inadequate variation of the negative class population. In OCSVM, we only need to feed the algorithm using the true class data, and the data with pattern that does not match will be classified as false. However, combining the two feature extractions produces many features, leading to the curse of dimensionality. The FS method is used to overcome this problem by selecting the best features. From the experiments carried out, the Haralick+LBP+FS+OCSVM model outperformed other models with an accuracy of 95.25% on validation data and 91.75% on test data.