期刊名称：Journal of Computing and Information Technology
印刷版ISSN：1330-1136
电子版ISSN：1846-3908
出版年度：2014
卷号：21
期号：4
页码：247-254
DOI：10.2498/cit.1002168
语种：English
出版社：SRCE - Sveučilišni računski centar
摘要：Many segmentation problems have been addressed using probabilistic modeling. These methods tend to estimate the region membership probabilities for each pixel of the image. The segmentation results depend strongly on the initialization of these regions and the selection of the appropriate number of segments. In this paper we present an unsupervised segmentation method based on non parametric clustering able to deal with these two issues. After a simple splitting, a minimum variance criterion is used to generate both the initial regions and their number. The proposed model was applied on various images (synthetic, natural) showing good visual results. Finally numerical experiments demonstrate the efficiency and the robustness of the proposed model compared to other segmentation methods.
关键词：image segmentation; non parametric clustering; class initialization; homogeneity
