http://www.cnr.it/ontology/cnr/individuo/prodotto/ID68495

Image classification via adaptive ensembles of descriptor-specific classifiers (Articolo in rivista)

Type

Label

Image classification via adaptive ensembles of descriptor-specific classifiers (Articolo in rivista) (literal)

Anno

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

Alternative label

Fagni T.; Falchi F.; Sebastiani F. (2010)
Image classification via adaptive ensembles of descriptor-specific classifiers
in Pattern recognition and image analysis; Maik Nauka/Interperiodica/Springer, New York (Stati Uniti d'America)
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Pagina inizio

Pagina fine

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

Rivista

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note

In: Pattern Recognition and Image Analysis, vol. 20 (1) pp. 21 - 28. Springer Verlag, 2010. (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

Note

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

Titolo

Image classification via adaptive ensembles of descriptor-specific classifiers (literal)

Abstract

An automated classification system usually consists of (i) a supervised learning algorithm for automatically generating classifiers from training data, and (ii) a representation scheme for converting the training objects into vectorial representations of their content. In this work, we take a detour from this tradition and present an approach to image classification based on an adaptive ensemble of classifiers, each specialized on classifying images based on a single \"descriptor\". Each descriptor focuses on a different aspect, or perspective, of images; an ensemble of descriptor-specific classifiers can thus be seen as a committee of experts, each viewing the problem to be solved with a different slant, of from a different viewpoint. We test four different ways to set up such an ensemble, based on different ways of leveraging on the individual responses returned by each member of the ensemble, and on how confident these members are on their responses. We test this approach by using five different MPEG-7 descriptors on the task of assigning photographs of stone slabs to classes representing different types of stones. Our experimental results show important accuracy improvements with respect to a baseline in which a single classifier, working an all five descriptors at the same time, is employed. (literal)

Editore