الگوریتم های خوشه بندی فازی با اطلاعات مکانی خود تنظیم غیر محلی برای تقسیم بندی تصویر
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|79170||2013||11 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Neurocomputing, Volume 106, 15 April 2013, Pages 115–125
Due to the limitation of the local spatial information in an image, fuzzy c-means clustering algorithms with the local spatial information cannot obtain the satisfying segmentation performance on the image heavily contaminated by noise. In order to compensate this drawback of the local spatial information, an effective kind of non-local spatial information is extracted from the image in this paper. In the acquisition of non-local spatial information, the filtering degree parameter h is a very crucial parameter and needs to be set appropriately. Instead of using a single h value for all the pixels, the calculation of the adaptive parameter h for each pixel is done by studying the statistical characteristics in its search window. Therefore, the non-local spatial information obtained by using the adaptive h value for each pixel is called self-tuning non-local spatial information. In this paper, two novel fuzzy clustering algorithms using the self-tuning non-local spatial information are proposed. In the first algorithm framework, a spatial constraint term by utilizing the self-tuning non-local spatial information for each pixel is defined and then introduced into the objective function of FCM. This algorithm is called fuzzy c-means clustering algorithm with self-tuning non-local spatial information (FCM_SNLS). In the second algorithm framework, a novel gray level histogram is constructed by using the self-tuning non-local spatial information for each pixel, and then clustering is performed on this gray level histogram. This algorithm is called fast fuzzy c-means clustering algorithm with self-tuning non-local spatial information (FFCM_SNLS). Experimental results show that these two proposed methods are not only more effective than fuzzy clustering algorithms with the local spatial information in noise suppression and edge preservation, but also more robust than fuzzy clustering algorithms with the non-local spatial information.