Skip to main content

Analysis and Modelling of Diversity Contribution to Ensemble-Based Texture Recognition Performance

Chindaro, Samuel and Sirlantzis, Konstantinos and Fairhurst, Michael (2005) Analysis and Modelling of Diversity Contribution to Ensemble-Based Texture Recognition Performance. In: Oza, N.C., ed. Multiple Classifier Systems 6th International Workshop. Lecture Notes in Computer Science . Springer, Berlin, Germany, pp. 387-396. ISBN 978-3-540-26306-7. E-ISBN 978-3-540-31578-0. (doi:10.1007/11494683_39) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:8779)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1007/11494683_39

Abstract

The RGB colour space is prominent as a colour representation and display scheme, although a number of other colour spaces have been developed over the years each with its own advantages and shortcomings with regard to its usefulness for colour/texture recognition. However, the recent advent of multiple classifier systems provides the unique opportunity to exploit the diverse information encapsulated in the different colour representations in a systematic fashion. In this paper we propose the use of classifier combination schemes which utilise information from different colour domains. We subsequently use suitable measures to investigate the diversity of the information infused by the different colour spaces. Experiments with two 40-class colour/texture datasets show the benefit of our multiple classifier approach, and reveal the existence of strong correlations between the accuracy achieved and the diversity measures. Finally, we illustrate, using quadratic regression, that there is significant scope to build and explore further (potentially causal) models of the observed relations between ensemble performance and diversity metrics. Our results point towards the use of diversity along with other statistical measures as possible predictors of the ensemble behaviour.

Item Type: Book section
DOI/Identification number: 10.1007/11494683_39
Uncontrolled keywords: Colour Space, Markov Random Field, Colour Representation, Fisher Linear Discriminant, Multiple Classifier System
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK7800 Electronics > TK7880 Applications of electronics > TK7882.P3 Pattern recognition systems
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Konstantinos Sirlantzis
Date Deposited: 16 Aug 2009 15:55 UTC
Last Modified: 16 Nov 2021 09:46 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/8779 (The current URI for this page, for reference purposes)

University of Kent Author Information

Chindaro, Samuel.

Creator's ORCID:
CReDIT Contributor Roles:

Sirlantzis, Konstantinos.

Creator's ORCID: https://orcid.org/0000-0002-0847-8880
CReDIT Contributor Roles:

Fairhurst, Michael.

Creator's ORCID:
CReDIT Contributor Roles:
  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.