Fuzzy color model and clustering algorithm for color clustering problem

TL;DR

This paper introduces a fuzzy color model and a novel clustering algorithm that effectively handle the inherent uncertainty in color data, improving color clustering accuracy.

Contribution

It proposes a fuzzy color model with a fuzzy color ball and a new fuzzy clustering algorithm for more efficient color data partitioning.

Findings

Enhanced clustering accuracy for arbitrary color data

Effective modeling of color vagueness and uncertainty

Improved partitioning efficiency

Abstract

The research interest of this paper is focused on the efficient clustering task for an arbitrary color data. In order to tackle this problem, we have tried to model the inherent uncertainty and vagueness of color data using fuzzy color model. By taking fuzzy approach to color modeling, we could make a soft decision for the vague regions between neighboring colors. The proposed fuzzy color model defined a three dimensional fuzzy color ball and color membership computation method with two inter-color distances. With the fuzzy color model, we developed a new fuzzy clustering algorithm for an efficient partition of color data. Each fuzzy cluster set has a cluster prototype which is represented by fuzzy color centroid.