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2017 | OriginalPaper | Buchkapitel

Image Aesthetic Quality Evaluation Using Convolution Neural Network Embedded Fine-Tune

verfasst von : Yuxin Li, Yuanyuan Pu, Dan Xu, Wenhua Qian, Lipeng Wang

Erschienen in: Computer Vision

Verlag: Springer Singapore

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Abstract

A way of convolution neural network (CNN) embedded fine-tune based on the image contents is proposed to evaluate the image aesthetic quality in this paper. Our approach can not only solve the problem of small-scale data but also quantify the image aesthetic quality. First, we chose Alexnet and VGG_S to compare which is more suitable for image aesthetic quality evaluation task. Second, to further boost the image aesthetic quality classification performance, we employ the image content to train aesthetic quality classification models. But the training samples become smaller and only using once fine-tune can not make full use of the small-scale dataset. Third, to solve the problem in second step, a way of using twice fine-tune continually based on the aesthetic quality label and content label respective, is proposed. At last, the categorization probability of the trained CNN models is used to evaluate the image aesthetic quality. We experiment on the small-scale dataset Photo Quality. The experiment results show that the classification accuracy rates of our approach are higher than the existing image aesthetic quality evaluation approaches.

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Vorheriges Kapitel An Error-Activation-Guided Blind Metric for Stitched Panoramic Image Quality Assessment

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Titel: Image Aesthetic Quality Evaluation Using Convolution Neural Network Embedded Fine-Tune
verfasst von: Yuxin Li
Yuanyuan Pu
Dan Xu
Wenhua Qian
Lipeng Wang
Verlag: Springer Singapore
Buch: Computer Vision
Print ISBN: 978-981-10-7301-4

Electronic ISBN: 978-981-10-7302-1

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-981-10-7302-1_23

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