Robust texture recognition using credal classifiers

Corani, Giorgio and Giusti, Alessandro and Migliore, Davide and Schmidhuber, Juergen (2010) Robust texture recognition using credal classifiers. In: Proceedings of the British Machine Vision Conference, 31.08.2010-03.09.2010, Aberystwyth.

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exture classification is used for many vision systems; in this paper we focus on improving the reliability of the classification through the so-called imprecise (or credal) classifiers, which suspend the judgment on the doubtful instances by returning a set of classes instead of a single class. Our view is that on critical instances it is more sensi- ble to return a reliable set of classes rather than an unreliable single class. We compare the traditional naive Bayes classifier (NBC) against its imprecise counterpart, the naive credal classifier (NCC); we consider a standard classification dataset, when the problem is made progressively harder by introducing different image degradations or by provid- ing smaller training sets. Experiments show that on the instances for which NCC returns more classes, NBC issues in fact unreliable classifications; the indeterminate classifica- tions of NCC preserve reliability but at the same time also convey significant information, reducing the set of possible classes (on most critical instances) from 24 to some 2-3.

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