Multi-level thresholding using entropy-based weighted FCM algorithm in color image

Authors:
Jun-Taek Oh;Hyun-Wook Kwak;Young-Ho Sohn;Wook-Hyun Kim
Affiliations:
School of EECS, Yeungnam University, Gyeongbuk, South Korea;School of EECS, Yeungnam University, Gyeongbuk, South Korea;School of EECS, Yeungnam University, Gyeongbuk, South Korea;School of EECS, Yeungnam University, Gyeongbuk, South Korea
Venue:
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Year:
2005

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Abstract

This paper proposes a multi-level thresholding method based on a weighted FCM(Fuzzy C-Means) algorithm in color image. FCM algorithm can determine a more optimal thresholding value than existing methods and be extended to multi-level thresholding, yet it is sensitive to noise, as it does not include spatial information. To solve this problem, a weight based on the entropy obtained from neighboring pixels is applied to FCM algorithm, and the optimal cluster number is determined using the within-class distance in the code image based on the clustered pixels for each color component. Experiments confirmed that the proposed method was more tolerant to noise and superior to existing methods.