Conventional Machine Learning Approach for Waste Classification

Waste management is a complex and challenging process, especially waste classification to sort waste by categories. The paper aims to overcome these challenges by proposing a waste classification approach that uses various feature extraction algorithms along with a support vector machine (SVM). The purpose is to identify the most effective feature for building a classification model, even with a low number of samples and high intra-class variance. SVM was used for classification while Fourier descriptors (FDs), histogram of oriented gradients (HOG), and local binary pattern (LBP) were used for feature extraction. The dataset used in this paper was obtained from Kaggle.com and Google.com with different types of vision problems. The experimental results showed that classification with LBP feature extraction achieves the highest accuracy. This accuracy is higher than the experiments with other feature extractions.