Please use this identifier to cite or link to this item: https://doi.org/10.3934/ipi.2011.5.407
Title: A regularized k-means and multiphase scale segmentation
Authors: Kang, S.H.
Sandberg, B.
Yip, A.M. 
Keywords: K-means
Multiphase
Scale
Segmentation
Variational model
Issue Date: May-2011
Citation: Kang, S.H., Sandberg, B., Yip, A.M. (2011-05). A regularized k-means and multiphase scale segmentation. Inverse Problems and Imaging 5 (2) : 407-429. ScholarBank@NUS Repository. https://doi.org/10.3934/ipi.2011.5.407
Abstract: We propose a data clustering model reduced from variational approach. This new clustering model, a regularized k-means, is an extension from the classical k-means model. It uses the sum-of-squares error for assessing fidelity, and the number of data in each cluster is used as a regularizer. The model automatically gives a reasonable number of clusters by a choice of a pa-rameter. We explore various properties of this classification model and present difierent numerical results. This model is motivated by an application to scale segmentation. A typical Mumford-Shah-based image segmentation is driven by the intensity of objects in a given image, and we consider image segmentation using additional scale information in this paper. Using the scale of objects, one can further classify objects in a given image from using only the intensity value. The scale of an object is not a local value, therefore the procedure for scale segmentation needs to be separated into two steps: multiphase segmentation and scale clustering. The first step requires a reliable multiphase segmentation where we applied unsupervised model, and apply a regularized k-means for a fast automatic data clustering for the second step. Various numerical results are presented to validate the model. © 2011 American Institute of Mathematical Sciences.
Source Title: Inverse Problems and Imaging
URI: http://scholarbank.nus.edu.sg/handle/10635/102745
ISSN: 19308337
DOI: 10.3934/ipi.2011.5.407
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