page_title: Fuzzy clustering
section_title: Image processing example
hierarchical_section_title: Fuzzy clustering / Image processing example
caption_attribution_description: English: Fuzzy Clustering example
context_page_description: Fuzzy clustering is a form of clustering in which each data point can belong to more than one cluster.
  Clustering or cluster analysis involves assigning data points to clusters such that items in the same cluster are as similar as possible, while items belonging to different clusters are as dissimilar as possible. Clusters are identified via similarity measures. These similarity measures include distance, connectivity, and intensity. Different similarity measures may be chosen based on the data or the application.
context_section_description: Image segmentation using k-means clustering algorithms has long been used for pattern recognition, object detection, and medical imaging. However, due to real world limitations such as noise, shadowing, and variations in cameras, traditional hard clustering is often unable to reliably perform image processing tasks as stated above.  Fuzzy clustering has been proposed as a more applicable algorithm in the performance to these tasks.  Given is gray scale image that has undergone fuzzy clustering in Matlab.  The original image is seen next to a clustered image.  Colors are used to give a visual representation of the three distinct clusters used to identify the membership of each pixel. Below, a chart is given that defines the fuzzy membership coefficients of their corresponding intensity values.
  Depending on the application for which the fuzzy clustering coefficients are to be used, different pre-processing techniques can be applied to RGB images.  RGB to HCL conversion is common practice.
caption: Image segmented by fuzzy clustering, with the original (top left), clustered (top right), and membership map (bottom)
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