Morphological classifiers

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.

arXiv commentPattern Recognition, 2018

References in corpus (7)

• Popular Ensemble Methods: An Empirical Study
• Combining Minkowski and Chebyshev: New distance proposal and survey of distance metrics using k-nearest neighbours classifier
• A novel approach for the automated segmentation and volume quantification of cardiac fats on computed tomography
• Machine learning in the prediction of cardiac epicardial and mediastinal fat volumes
• k-MS: A novel clustering algorithm based on morphological reconstruction
• Automated recognition of the pericardium contour on processed CT images using genetic algorithms
• Comparing Results of Thermographic Images Based Diagnosis for Breast Diseases

Cited by in corpus (9)

• ELEMENT: Multi-Modal Retinal Vessel Segmentation Based on a Coupled Region Growing and Machine Learning Approach
• Identifying cybersickness causes in virtual reality games using symbolic machine learning algorithms
• Cardiac fat segmentation using computed tomography and an image-to-image conditional generative adversarial neural network
• Line Art Colorization of Fakemon using Generative Adversarial Neural Networks
• Machine learning applied to emerald gemstone grading: framework proposal and creation of a public dataset
• Local-sensitive connectivity filter (ls-cf): A post-processing unsupervised improvement of the frangi, hessian and vesselness filters for multimodal vessel segmentation
• X-Ray cardiac angiographic vessel segmentation based on pixel classification using machine learning and region growing
• A Symbolic Machine Learning Approach for Cybersickness Potential-Cause Estimation
• A Novel Approach for the Counting of Wood Logs Using cGANs and Image Processing Techniques