IEEE Trans Image Process - A robust fuzzy local information C-Means clustering algorithm.

Tópicos

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{ learn(2355) train(1041) set(1003) }
{ case(1353) use(1143) diagnosi(1136) }
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{ high(1669) rate(1365) level(1280) }
{ survey(1388) particip(1329) question(1065) }
{ activ(1452) weight(1219) physic(1104) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

This paper presents a variation of fuzzy c-means (FCM) algorithm that provides image clustering. The proposed algorithm incorporates the local spatial information and gray level information in a novel fuzzy way. The new algorithm is called fuzzy local information C-Means (FLICM). FLICM can overcome the disadvantages of the known fuzzy c-means algorithms and at the same time enhances the clustering performance. The major characteristic of FLICM is the use of a fuzzy local (both spatial and gray level) similarity measure, aiming to guarantee noise insensitiveness and image detail preservation. Furthermore, the proposed algorithm is fully free of the empirically adjusted parameters (a, ?(g), ?(s), etc.) incorporated into all other fuzzy c-means algorithms proposed in the literature. Experiments performed on synthetic and real-world images show that FLICM algorithm is effective and efficient, providing robustness to noisy images.

Resumo Limpo

paper present variat fuzzi cmean fcm algorithm provid imag cluster propos algorithm incorpor local spatial inform gray level inform novel fuzzi way new algorithm call fuzzi local inform cmean flicm flicm can overcom disadvantag known fuzzi cmean algorithm time enhanc cluster perform major characterist flicm use fuzzi local spatial gray level similar measur aim guarante nois insensit imag detail preserv furthermor propos algorithm fulli free empir adjust paramet g s etc incorpor fuzzi cmean algorithm propos literatur experi perform synthet realworld imag show flicm algorithm effect effici provid robust noisi imag

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