Robust texture recognition using credal classifiers

Giorgio Corani, Alessandro Giusti, Davide Migliore, Juergen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Texture 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 sensible 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 providing smaller training sets. Experiments show that on the instances for which NCC returns more classes, NBC issues in fact unreliable classifications; the indeterminate classifications 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. © 2010. The copyright of this document resides with its authors.
Original languageEnglish (US)
Title of host publicationBritish Machine Vision Conference, BMVC 2010 - Proceedings
PublisherBritish Machine Vision Association, BMVA
StatePublished - Jan 1 2010
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14


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