The Categorisation of Similar Non-rigid Biological Objects by Clustering Local Appearance Patches

  • Hongbin Wang
  • Phil F. Culverhouse
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3177)


A novel approach is presented to the categorisation of non-rigid biological objects from unsegmented scenes in an unsupervised manner. The biological objects investigated are five phytoplankton species from the coastal waters of the European Union. The high morphological variability within each species and the high similarity between species make the categorisation task a challenge for both marine ecologists and machine vision systems. The framework developed takes a local appearance approach to learn the object model, which is done using a novel-clustering algorithm with minimal supervised information. Test objects are classified based on matches with local patches of high occurrence. Experiments show that the method achieves good results, given the difficulty of the task.


Interest Point Local Patch Agglomerative Hierarchical Cluster Machine Vision System Characteristic Patch 
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 2004

Authors and Affiliations

  • Hongbin Wang
    • 1
  • Phil F. Culverhouse
    • 1
  1. 1.Centre for Interactive Intelligent Systems, School of Computing, Communications and ElectronicsUniversity of PlymouthPlymouthUK

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