The performance of Machine Learning on Low Resolution Image Classifier

Abstract

The ability of machine learning has become a very famous and important technique for discovering statistically significant patterns in the available data. In this paper, we presented the gradient profile spectral characteristics classification on vertical and horizontal gradient acceleration data, Edge Sketch Image and The Relational Gradient Direction data in low-resolution image input. Various training datasets were learned by CatBoost Classifier to created gradient profile priors. This technique was boosting schemes help to reduce over fitting and improves quality of the model. Due to symmetric tree structure of the CatBoost, it provided fast inference and accelerated the implementation. Several predictive and conventional classification techniques were chosen for performance comparison. The experimental results demonstrated performance improvement in classification of the frequency level area in various image characteristics.