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Unsupervised Learning of Image Recognition with Neural Society for Clustering

  • Marcin Wojnarski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4259)

Abstract

New algorithm for partitional data clustering is presented, Neural Society for Clustering (NSC). Its creation was inspired by hierarchical image understanding, which requires unsupervised training to build the hierarchy of visual features. Existing clustering algorithms are not well-suited for this task, since they usually split natural groups of patterns into several parts (like k-means) or give crisp clustering.

Neurons comprising NSC may be viewed as a society of autonomous individuals, proceeding along the same simple algorithm, based on four principles: of locality, greediness, balance and competition. The same principles govern large groups of entities in economy, sociology, biology and physics. Advantages of NSC are demonstrated in experiment with visual data. The paper presents also a new method for objective and quantitative comparison of clustering algorithms, based on the notions of entropy and mutual information.

Keywords

Mutual Information Cluster Center Visual Feature Gaussian Mixture Model Unsupervised Learn 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marcin Wojnarski
    • 1
  1. 1.Faculty of Mathematics, Informatics and MechanicsWarsaw UniversityWarszawaPoland

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