Classifier ensembles for image identification using multi-objective Pareto features

NEUROCOMPUTING

卷:238

Article

Albukhanajer, Wissam A.¹  Jin, Yaochu²  Briffa, Johann A.³ 

[1] Univ Surrey, Fac Engn & Phys Sci, Surrey Space Ctr, Guildford GU2 7XH, Surrey, England

[2] Univ Surrey, Fac Engn & Phys Sci, Dept Comp Sci, Guildford GU2 7XH, Surrey, England

[3] Univ Malta, Fac Informat & Commun Technol, Dept Commun & Comp Engn, Msida 2080, Malta

关键词: Pareto front;    Classifier ensembles;    Majority voting;    Image identification;    Trace transform;    Evolutionary multi-objective optimization;   

DOI  :  10.1016/j.neucom.2017.01.067

来源: Elsevier

【 摘 要 】

In this paper we propose classifier ensembles that use multiple Pareto image features for invariant image identification. Different from traditional ensembles that focus on enhancing diversity by generating diverse base classifiers, 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 classifiers 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, the proposed ensembles using multiple Pareto features perform much better than both, the traditional classifier ensembles of single Pareto features with data randomization, and the well-known Random Forest ensemble. The better classification 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 classifier ensembles are robust to various geometric transformations in images such as rotation, scale and translation, and to additive noise. (C) 2017 Published by Elsevier B.V.

【 授权许可】

Free