Anzeige
Erschienen in:
2008 | OriginalPaper | Buchkapitel
Noisy Image Segmentation by a Robust Clustering Algorithm Based on DC Programming and DCA
verfasst von : Le Thi Hoai An, Le Hoai Minh, Nguyen Trong Phuc, Pham Dinh Tao
Erschienen in: Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
We present a fast and robust algorithm for image segmentation problems via Fuzzy C-Means (FCM) clustering model. Our approach is based on DC (Difference of Convex functions) programming and DCA (DC Algorithms) that have been successfully applied in a lot of various fields of Applied Sciences, including Machine Learning. In an elegant way, the FCM model is reformulated as a DC program for which a very simple DCA scheme is investigated. For accelerating the DCA, an alternative FCM-DCA procedure is developed. Moreover, in the case of noisy images, we propose a new model that incorporates spatial information into the membership function for clustering. Experimental results on noisy images have illustrated the effectiveness of the proposed algorithm and its superiority with respect to the standard FCM algorithm in both running-time and quality of solutions.