Please use this identifier to cite or link to this item:
|Title:||A regularized k-means and multiphase scale segmentation||Authors:||Kang, S.H.
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Apr 9, 2020
WEB OF SCIENCETM
checked on Jul 5, 2019
checked on Mar 28, 2020
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.