Image segmentation aims at identifying regions of interest within an image by grouping pixels according to their properties. This task resembles the statistical one of clustering, yet many standard clustering methods fail to meet the basic requirements of image segmentation since the identified segments are often biased toward predetermined shapes and their number is rarely determined automatically. Nonparametric clustering is, in principle, free from these limitations and particularly suitable for the task of image segmentation. We discuss the application of nonparametric clustering to image segmentation and provide an algorithm specific for this task. Pixel similarity is evaluated in terms of the density of the color representation. The adjacency structure of the pixels is exploited to introduce a simple, yet effective method to identify image segments as disconnected high‐density regions. The proposed method answers to the need of both segmenting an image and detecting its boundaries and can be seen as a generalization to color images of the class of thresholding methods.
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