Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

A novel one-vs-rest classification framework for mutually supported decisions by independent parallel classifiers

Vogiatzis Antonios, Chalkiadakis Georgios, Moirogiorgou Konstantia, Zervakis Michail

Simple record


URIhttp://purl.tuc.gr/dl/dias/DE097300-EE7A-4B42-9469-3DD686BF82C3-
Identifierhttps://doi.org/10.1109/IST50367.2021.9651468-
Identifierhttps://ieeexplore.ieee.org/document/9651468-
Languageen-
Extent6 pagesen
TitleA novel one-vs-rest classification framework for mutually supported decisions by independent parallel classifiersen
CreatorVogiatzis Antoniosen
CreatorΒογιατζης Αντωνιοςel
CreatorChalkiadakis Georgiosen
CreatorΧαλκιαδακης Γεωργιοςel
CreatorMoirogiorgou Konstantiaen
CreatorΜοιρογιωργου Κωνσταντιαel
CreatorZervakis Michailen
CreatorΖερβακης Μιχαηλel
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryWe put forward a generic classification architecture of independent parallel CNNs that explicitly exploits a “mutual exclusivity” or “classifiers’ mutually supported decisions” property underlying many dataset domains of interest, namely that in many cases an image in a given dataset might almost unquestionably belong to one class only. Our framework incorporates several designed-to-purpose opinion aggregation decision rules that are triggered when the mutual exclusivity property is or is not satisfied; and makes use of “weights” which intuitively mirror the confidence each CNN has in identifying its corresponding class. Our framework can thus (a) take advantage of clear class boundaries when these exist, and (b) effectively assign items to classes with increased confidence, even when clear class boundaries do not exist. We confirm the effectiveness of our approach via experiments conducted on a well-known dataset from the waste classification domain.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2023-05-11-
Date of Publication2021-
SubjectImage classificationen
SubjectSupervised learningen
SubjectMutual exclusivityen
SubjectDecision rulesen
SubjectOpinion aggregationen
Bibliographic CitationA. Vogiatzis, G. Chalkiadakis, K. Moirogiorgou and M. Zervakis, "A novel one-vs-rest classification framework for mutually supported decisions by independent parallel classifiers," presented at the 2021 IEEE International Conference on Imaging Systems and Techniques (IST), Kaohsiung, Taiwan, 2021, doi: 10.1109/IST50367.2021.9651468.en

Services

Statistics