Abstract
In this article we learn significant local appearance features for visual classes. Generic feature detectors are obtained by unsupervised learning using clustering. The resulting clusters, referred to as “classtons”, identify the significant class characteristics from a small set of sample images. The classton channels mark these characteristics reliably using a probabilistic cluster representation. The classtons demonstrate good generalisation with respect to viewpoint changes and previously unseen objects. In all experiments, the classton channels of similar images have the same spatial relations. Learning of these relations allows to generate a classification model that combines the generalisation ability from the classtons and the discriminative power from the spatial relations.
This research is funded by IST CAVIAR 2001 37540
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Hall, D., Crowley, J.L. (2003). Computation of Generic Features for Object Classification. In: Griffin, L.D., Lillholm, M. (eds) Scale Space Methods in Computer Vision. Scale-Space 2003. Lecture Notes in Computer Science, vol 2695. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44935-3_52
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DOI: https://doi.org/10.1007/3-540-44935-3_52
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