A clustering algorithm based on maximal θ-distant subtrees
Pattern Recognition
International Journal of Autonomous and Adaptive Communications Systems
Vector Quantization of Images Using a Fuzzy Clustering Method
Cybernetics and Systems
ART2 neural network interacting with environment
Neurocomputing
Extracting and predicting the communication behaviour of parallel applications
International Journal of Parallel, Emergent and Distributed Systems
Colour image segmentation using the self-organizing map and adaptive resonance theory
Image and Vision Computing
An adaptive ant-based clustering algorithm with improved environment perception
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Color reduction using a multi-stage Kohonen Self-Organizing Map with redundant features
Expert Systems with Applications: An International Journal
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A modified adaptive resonance theory (ART2) learning algorithm, which we employ in this paper, belongs to the family of NN algorithms whose main goal is the discovery of input data clusters, without considering their actual size. This feature makes the modified ART2 algorithm very convenient for image compression tasks, particularly when dealing with images with large background areas containing few details. Moreover, due to the ability to produce hierarchical quantization (clustering), the modified ART2 algorithm is proved to significantly reduce the computation time required for coding, and therefore enhance the overall compression process. Examples of the results obtained are presented, suggesting the benefits of using this algorithm for the purpose of VQ, i.e., image compression, over the other NN learning algorithms