Abstract
The problem of comparing images or image regions can be considered as the problem of matching unordered sets of high dimensional visual features. We show that an hierarchical Growing Neural Gas (GNG) can robustly be used to approximate the optimal partial matching cost between vector sets. Further, we extend the unordered set matching, such that the matching of local features pays attention to the structure of the object and the relative positions of the parts. This view-tuning is also realized with hierarchical GNGs and yields an efficient Mercer Kernel.
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Kortkamp, M., Wachsmuth, S. (2011). View-Tuned Approximate Partial Matching Kernel from Hierarchical Growing Neural Gases. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21738-8_56
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DOI: https://doi.org/10.1007/978-3-642-21738-8_56
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