Visualisation and comparison of image collections based on self-organised maps

ABSTRACT

Self-organised maps (SOM) have been widely used for cluster analysis and visualisation purposes in exploratory data mining. In image retrieval applications, SOMs have been used to visualise high-dimensional feature space and build indexing structures. In this paper, we extend the use of SOMs for profiling and comparison of image collections, and present empirical results obtained in collection visualisation, visual and quantitative comparison of collections, and a prototype system implementation.

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Index Terms

• Computer systems organization

  • Architectures

    • Other architectures

      • Neural networks

• Computing methodologies

  • Artificial intelligence

    • Computer vision

      • Computer vision problems

        • Object recognition

  • Machine learning

    • Learning paradigms

      • Unsupervised learning

        • Cluster analysis

    • Machine learning approaches

      • Neural networks