A clustering fuzzy approach for image segmentation

Author(s): Cinque, L. | Foresti, G. | Lombardi, L. |

Year: 2004

Citation: PATTERN RECOGNITION Volume: 37 Issue: 9 Pages: 1797-1807

Abstract: Segmentation is a fundamental step in image description or classification. In recent years, several computational models have been used to implement segmentation methods but without establishing a single analytic solution. However, the intrinsic properties of neural networks make them an interesting approach, despite some measure of inefficiency. This paper presents a clustering approach for image segmentation based on a modified fuzzy approach for image segmentation (ART) model. The goal of the proposed approach is to find a simple model able to instance a prototype for each cluster avoiding complex post-processing phases. Results and comparisons with other similar models presented in the literature (like self-organizing maps and original fuzzy ART) are also discussed. Qualitative and quantitative evaluations confirm the validity of the approach proposed.

Topics: Image Analysis, Applications: Information Fusion, Models: ART 2 / Fuzzy ART, Self Organizing Maps,

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