Morphological classifiers

TL;DR

This paper introduces Morphological Classifiers, combining mathematical morphology and supervised learning, offering fast classification with competitive accuracy, demonstrated through two models tested on multiple datasets.

Contribution

It presents a novel class of classifiers based on set theory and morphology, with two specific models, MkNN and MDC, showing promising performance and GPU acceleration.

Findings

MCs achieved higher than average accuracy in most datasets.

MkNN and MDC outperformed or tied with 14 established classifiers.

The methods are computationally efficient due to GPU implementation.

Abstract

This work proposes a new type of classifier called Morphological Classifier (MC). MCs aggregate concepts from mathematical morphology and supervised learning. The outcomes of this aggregation are classifiers that may preserve shape characteristics of classes, subject to the choice of a stopping criterion and structuring element. MCs are fundamentally based on set theory, and their classification model can be a mathematical set itself. Two types of morphological classifiers are proposed in the current work, namely, Morphological k-NN (MkNN) and Morphological Dilation Classifier (MDC), which demonstrate the feasibility of the approach. This work provides evidence regarding the advantages of MCs, e.g., very fast classification times as well as competitive accuracy rates. The performance of MkNN and MDC was tested using p -dimensional datasets. MCs tied or outperformed 14 well established classifiers in 5 out of 8 datasets. In all occasions, the obtained accuracies were higher than the average accuracy obtained with all classifiers. Moreover, the proposed implementations utilize the power of the Graphics Processing Units (GPUs) to speed up processing.