A robust two-stage system for image segmentation

This paper proposes a new method to split images into regions. It consists of two subsystems: cluster detection and cluster fusion. The cluster detection is performed by a competitive neural network or the k-means algorithm, followed by an algorithm which obtains connected clusters. The cluster fusi...

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Bibliographic Details
Main Authors: Lopez-Rubio, E., Munoz-Perez, J., Gomez-Ruiz, J.A.
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Clustering methods
Computer vision
Gaussian distribution
Gaussian processes
Image segmentation
Neural networks
Partitioning algorithms
Pixel
Robustness
Online Access:Request full text
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Description
Summary:This paper proposes a new method to split images into regions. It consists of two subsystems: cluster detection and cluster fusion. The cluster detection is performed by a competitive neural network or the k-means algorithm, followed by an algorithm which obtains connected clusters. The cluster fusion involves a procedure that is based on the theory of equivalence relations. Proofs are given for the significant properties that we have found. It is not necessary to specify the number of regions in advance, which is a significant improvement over the standard competitive-style strategies. Finally, simulation results are given to demonstrate the performance of this method for some images.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2000.905410