In:
Mathematical Problems in Engineering, Hindawi Limited, Vol. 2020 ( 2020-03-11), p. 1-10
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.
Type of Medium:
Online Resource
ISSN:
1024-123X
,
1563-5147
DOI:
10.1155/2020/1245924
Language:
English
Publisher:
Hindawi Limited
Publication Date:
2020
detail.hit.zdb_id:
2014442-8
SSG:
11
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