PLANET: Improved Convolutional Neural Networks with Image Enhancement for Image Classification

Author:

Tang Chaohui12ORCID,Zhu Qingxin1,Wu Wenjun2ORCID,Huang Wenlin2,Hong Chaoqun2ORCID,Niu Xinzheng13ORCID

Affiliation:

1. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

2. School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China

3. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

Abstract

In the past few years, deep learning has become a research hotspot and has had a profound impact on computer vision. Deep CNN has been proven to be the most important and effective model for image processing, but due to the lack of training samples and huge number of learning parameters, it is easy to tend to overfit. In this work, we propose a new two-stage CNN image classification network, named “Improved Convolutional Neural Networks with Image Enhancement for Image Classification” and PLANET in abbreviation, which uses a new image data enhancement method called InnerMove to enhance images and augment the number of training samples. InnerMove is inspired by the “object movement” scene in computer vision and can improve the generalization ability of deep CNN models for image classification tasks. Sufficient experiment results show that PLANET utilizing InnerMove for image enhancement outperforms the comparative algorithms, and InnerMove has a more significant effect than the comparative data enhancement methods for image classification tasks.

Funder

Ministry of Education Pre-Research Foundation

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

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