Robust Texture Recognition Using Credal Classifiers

9 years 5 months ago
Robust Texture Recognition Using Credal Classifiers
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 pos...
Giorgio Corani, Alessandro Giusti, Davide Migliore
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where BMVC
Authors Giorgio Corani, Alessandro Giusti, Davide Migliore, Jürgen Schmidhuber
Comments (0)