The enhanced LBG algorithm

Clustering applications cover several fields such as audio and video data compression, pattern recognition, computer vision, medical image recognition, etc. In this paper, we present a new clustering algorithm called Enhanced LBG (ELBG). It belongs to the hard and K-means vector quantization groups...

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Bibliographic Details
Published in:Neural networks 2001-11, Vol.14 (9), p.1219-1237
Main Authors: Patané, Giuseppe, Russo, Marco
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Clustering
Computer science
control theory
systems
Connectionism. Neural networks
Exact sciences and technology
Fuzzy c-means
GLA
Hard c-means
Image Processing, Computer-Assisted - methods
K-means
LBG
LVQ
Neural Networks (Computer)
Pattern recognition. Digital image processing. Computational geometry
Stochastic Processes
Unsupervised learning
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Summary:Clustering applications cover several fields such as audio and video data compression, pattern recognition, computer vision, medical image recognition, etc. In this paper, we present a new clustering algorithm called Enhanced LBG (ELBG). It belongs to the hard and K-means vector quantization groups and derives directly from the simpler LBG. The basic idea we have developed is the concept of utility of a codeword, a powerful instrument to overcome one of the main drawbacks of clustering algorithms: generally, the results achieved are not good in the case of a bad choice of the initial codebook. We will present our experimental results showing the ELBG is able to find better codebooks than previous clustering techniques and the computational complexity is virtually the same as the simpler LBG.
ISSN:0893-6080
1879-2782
DOI:10.1016/S0893-6080(01)00104-6