Show simple item record

Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification

dc.date.accessioned2004-10-08T20:34:28Z
dc.date.accessioned2018-11-24T10:15:42Z
dc.date.available2004-10-08T20:34:28Z
dc.date.available2018-11-24T10:15:42Z
dc.date.issued1993-01-01en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/6613
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/6613
dc.description.abstractIn this paper we present some extensions to the k-means algorithm for vector quantization that permit its efficient use in image segmentation and pattern classification tasks. It is shown that by introducing state variables that correspond to certain statistics of the dynamic behavior of the algorithm, it is possible to find the representative centers fo the lower dimensional maniforlds that define the boundaries between classes, for clouds of multi-dimensional, mult-class data; this permits one, for example, to find class boundaries directly from sparse data (e.g., in image segmentation tasks) or to efficiently place centers for pattern classification (e.g., with local Gaussian classifiers). The same state variables can be used to define algorithms for determining adaptively the optimal number of centers for clouds of data with space-varying density. Some examples of the applicatin of these extensions are also given.en_US
dc.format.extent95179 bytes
dc.format.extent1158231 bytes
dc.language.isoen_US
dc.titleSome Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classificationen_US


Files in this item

FilesSizeFormatView
AIM-1390.pdf1.158Mbapplication/pdfView/Open
AIM-1390.ps.Z95.17Kbapplication/octet-streamView/Open

This item appears in the following Collection(s)

Show simple item record