Improved Unsupervised Color Segmentation Using a Modified HSV Color Model and a Bagging Procedure in K-Means++ Algorithm

Received Oct 6, 2017; Revised Nov 24, 2017; Accepted Jan 2, 2018

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

Machine learning application area is growing every day, so it is possible to find applications using machine learning in health areas [1–5], system behavior predictions [6–10], image and video analysis [11–15], and speech and writing recognition [16–20], just to mention some of the most notable and recent applications. Some of the results of these advances in machine learning can be appreciated in several applications that are widely and freely available. As a result, the usage of machine learning has become a common component in the daily modern life.

Despite the huge improvements done in machine learning in the recent years, it still requires more work and research. This can be stated from the fact that a total accuracy is not yet achieved by machine learning, and sometimes the results are still not usable. This is the reason for the present proposal, which is developed in the spirit of improving a common process performed in image analysis using machine learning.

This paper gives an overview of the color models by stating their advantages and problems found when they are implemented or are used as input in other processes. A second topic discussed in the color models overview is the concept of chromatic and achromatic separation by the exclusion or mitigation of the illumination component. The latter topic has significant relevance, since many of the issues found in color detection and segmentation come from the fact that the illumination can change the perception from a color ranging from bright white to black, visiting several tones of the same base color. The overview will set the ground on which some changes are made to the $HSV$ perceptual color model.

Also a section regarding machine leaning algorithms is included, where the relevance of unsupervised learning is explained. Also in this section what issues can be found in the popular $K$-means algorithms is explained, as well as of course some existing techniques used to improve the classification results obtained from the usage of...