30 December 2003 Density-based unsupervised classification for spherical objects
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Human interpreters are very sensitive to spatial information in supervised classification. A well-known isodata algorithm in unsupervised classification requires many parameters to be set by human being. Some other unsupervised algorithm focuses on spectral information, but spatial information is lost in the process. Biased sampling is one good approach to get some information about the global structure. For local structures, many techniques have been used. For example similarity and local density are discussed in many papers. In biased sampling, images are divided into many l x l patches and a sample pixel is selected from each patch. Similarity at a point p, denoted by sim(p), measures the change of gray level between point p and its neighborhood N(p). In this article we introduce a method to use biased sampling to combine spectral and spatial information. We use similarity and local popularity in selecting sample points to get better results. To use similarity (sim(p)≤δ), one must determine δ. One way is to make it adapted such that a sample point can be selected from each patch. Here after normalization, we choose a sample point with a minimum value of [equation] for some positive numbers α and β. There is no precondition for δ needed and the selected pixel is a better representative, especially near the border of an object. Kernel estimators are employed to obtain smooth density approximation before final classification. Some experiments have been conducted using the proposed methods and the results are satisfactory.
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Shangrong Deng, Shangrong Deng, Kai Qian, Kai Qian, Chih-Cheng Hung, Chih-Cheng Hung, } "Density-based unsupervised classification for spherical objects", Proc. SPIE 5200, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI, (30 December 2003); doi: 10.1117/12.505070; https://doi.org/10.1117/12.505070

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