Abstract
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.
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Wang, H., Culverhouse, P.F. (2004). The Categorisation of Similar Non-rigid Biological Objects by Clustering Local Appearance Patches. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_10
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DOI: https://doi.org/10.1007/978-3-540-28651-6_10
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