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Research Article | Open Access

Kernel-blending connection approximated by a neural network for image classification

Shandong University of Finance and Economics, Jinan 250014, China
Shandong University, Jinan 250100, China
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Abstract

This paper proposes a kernel-blending connection approximated by a neural network (KBNN) for image classification. A kernel mapping connection structure, guaranteed by the function approximation theorem, is devised to blend feature extraction and feature classification through neural network learning. First, a feature extractor learns features from the raw images. Next, an automatically constructed kernel mapping connection maps the feature vectors into a feature space. Finally, a linear classifier is used as an output layer of the neural network to provide classification results. Furthermore, a novel loss function involving a cross-entropy loss and a hinge loss is proposed to improve the generalizability of the neural network. Experimental results on three well-known image datasets illustrate that the proposed method has good classification accuracy and generalizability.

Keywords

image classificationblending neural networkfunction approximationkernel mapping connectiongeneralizability

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Computational Visual Media
Volume 6 Issue 4,
December 2020
Pages 467-476
DOI: 10.1007/s41095-020-0181-9
Cite this article:
Liu X, Zhang Y, Bao F, et al. Kernel-blending connection approximated by a neural network for image classification. Computational Visual Media, 2020, 6(4): 467-476. https://doi.org/10.1007/s41095-020-0181-9

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Received: 31 March 2020
Accepted: 18 May 2020
Published: 14 September 2020
© The Author(s) 2020

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