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Classifer Ensembles for Image Identifcation Using Multi-objective Pareto Features

Albukhanajer, Wissam, Jin, Yaochu and Briffa, JA (2017) Classifer Ensembles for Image Identifcation Using Multi-objective Pareto Features Neurocomputing, 238. pp. 316-327.

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Abstract

In this paper we propose classi er ensembles that use multiple Pareto image features for invariant image identi cation. Di erent from traditional ensembles that focus on enhancing diversity by generating diverse base classi ers, the proposed method takes advantage of the diversity inherent in the Pareto features extracted using a multi-objective evolutionary Trace Transform algorithm. Two variants of the proposed approach have been implemented, one using multilayer perceptron neural networks as base classi ers and the other k-Nearest Neighbor. Empirical results on a large number of images from the Fish-94 and COIL-20 datasets show that on average, ensembles using Pareto features perform much better than traditional classi er ensembles using the same features and data randomization. The better classi cation performance of the proposed ensemble is further supported by diversity analysis using a number of measures, indicating that the proposed ensemble consistently produces a higher degree of diversity than traditional ones. Our experimental results demonstrate that the proposed classi er ensembles are robust to various geometric transformations in images such as rotation, scale and translation, and to additive noise.

Item Type: Article
Subjects : Computer Science
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Albukhanajer, WissamUNSPECIFIEDUNSPECIFIED
Jin, YaochuYaochu.Jin@surrey.ac.ukUNSPECIFIED
Briffa, JAUNSPECIFIEDUNSPECIFIED
Date : 8 February 2017
Identification Number : 10.1016/j.neucom.2017.01.067
Copyright Disclaimer : © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords : Pareto front, classi er ensembles, majority voting, image identi cation, Trace transform, evolutionary multi-objective optimization
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 01 Mar 2017 16:30
Last Modified : 06 Jul 2017 06:47
URI: http://epubs.surrey.ac.uk/id/eprint/813662

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