Abstract
In this study, we consider low-level image classification, with several machine learning algorithms adapted to high dimension problems: kernel-based algorithms. The first is Support Vector Machines (SVM), the second is Bayes Point Machines (BPM). We compare these algorithms based on strong mathematical results and nice geometrical arguments in a feature space to the simplest algorithm we could imagine working on the same representation. We use different low-level data, experimenting low-level preprocessing, including spatial information. Our results suggest that the kernel representation is more important than the algorithms used (at least for this task). It is a positive result because it exists much more simpler and faster algorithms than SVM. Our additive low-level preprocessings only improved success rate by few percents.
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Teytaud, O., Sarrut, D. (2001). Kernel Based Image Classification. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_52
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DOI: https://doi.org/10.1007/3-540-44668-0_52
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