Quantitative Analysis of MR Brain Image Sequences by Adaptive Self-Organizing Finite Mixtures
Journal of VLSI Signal Processing Systems - special issue on applications of neural networks in biomedical image processing
A generalized content-based image retrieval system
SAC '98 Proceedings of the 1998 ACM symposium on Applied Computing
Learning to Perceive Objects for Autonomous Navigation
Autonomous Robots
Computing Content-Plots for Video
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
A k-Median Algorithm with Running Time Independent of Data Size
Machine Learning
Classified Vector Quantisation and population decoding for pattern recognition
International Journal of Artificial Intelligence and Soft Computing
3PRS: a personalized popular program recommendation system for digital TV for P2P social networks
Multimedia Tools and Applications
Constructing NURBS surface model from scattered and unorganized range data
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Partitive clustering (K-means family)
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
A cloud-based intelligent TV program recommendation system
Computers and Electrical Engineering
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We present some extensions to the k-means algorithm for vector quantization that permit its efficient use in image segmentation and pattern classification tasks. We show that by introducing a certain set of state variables it is possible to find the representative centers of the lower dimensional manifolds that define the boundaries between classes; this permits one, for example, to find class boundaries directly from sparse data or to efficiently place centers for pattern classification. The same state variables can be used to determine 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.