Solution of High-level Classification Task by Low-level Classifier in Vehicle Recognition Problem

The study deals with machine learning and computer vision, emphasizing the importance of understanding both low-level and high-level information of classifiers. The paper presents a special multi-output classification, where there are only two independent labels, the color and the type of vehicles in images. For the solution of the multi-output classification task, a new approach was examined, a multi-class classifier with single output. The original multi-output classification is considered as high-level task, while the single output classifier with all combination of color and type classes is a low-level classifier, because its output (a single label) is more detailed. The novelty of the work is the comparison of both approaches, the original high-level classifiers with low-level classifier. The contribution of the paper is the different transformation methods (e.g. stacking-based) from low-level classifier to original high-level classification task. Our results show that low-level classifiers can be a viable alternative, in particular the stacking-based method can enhance the classification accuracy. ©2025 IEEE.