Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification
Abstract
In 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 of the lower dimensional manifolds that define the boundaries between classes, for clouds of multi- dimensional, multi-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 application of these extensions are also given. K-Means, Vector quantization, Classification, Clustering, Segmentation.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1993
- Accession Number
- ADA271691
Entities
People
- Federico Girosi
- Jose L. Marroquin
Organizations
- Massachusetts Institute of Technology