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Shape recognition through multi-level fusion of features and classifiers

Wang, Xinming, Ding, Weili, Liu, Han and Huang, Xiangsheng 2020. Shape recognition through multi-level fusion of features and classifiers. Granular Computing 5 , pp. 437-448. 10.1007/s41066-019-00164-8

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Abstract

Shape recognition is a fundamental problem and a special type of image classification, where each shape is considered as a class. Current approaches to shape recognition mainly focus on designing low-level shape descriptors, and classify them using some machine learning approaches. In order to achieve effective learning of shape features, it is essential to ensure that a comprehensive set of high quality features can be extracted from the original shape data. Thus we have been motivated to develop methods of fusion of features and classifiers for advancing the classification performance. In this paper, we propose a multi-level framework for fusion of features and classifiers in the setting of gran-ular computing. The proposed framework involves creation of diversity among classifiers, through adopting feature selection and fusion to create diverse feature sets and to train diverse classifiers using different learn-Xinming Wang algorithms. The experimental results show that the proposed multi-level framework can effectively create diversity among classifiers leading to considerable advances in the classification performance.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: Springer
ISSN: 2364-4974
Date of First Compliant Deposit: 18 April 2019
Date of Acceptance: 28 March 2019
Last Modified: 25 Nov 2020 20:21
URI: http://orca-mwe.cf.ac.uk/id/eprint/121825

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