Fuzzy color model and clustering algorithm for color clustering problem
This work addresses color clustering for applications like image processing, but it appears incremental as it builds on existing fuzzy methods.
The paper tackled the problem of clustering arbitrary color data by modeling color uncertainty with a fuzzy color model, resulting in a new fuzzy clustering algorithm that uses fuzzy color centroids as cluster prototypes.
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.