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Proceedings Paper

The objective assessment of aesthetic using improved convolution neural network
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Paper Abstract

This paper proposes an improved neural network model based on CNN for evaluating the aesthetic quality of images. Firstly, we create three input sources from different angles of the target image and establish a three-channel parallel network model. Secondly, mlpconv layer is used to replace the traditional linear convolution layer to obtain more nonlinear abstract features in the network model based on CNN; Then, the combination of the global average pooling and full connection layers are used to replace the full connection layer in traditional CNN, and the three-channel features are merged. Finally, the EMD function is used as the loss function in the softmax layer. The output is probability density mass function from 1 to 10, and the mean and variance are used as objective qualitative score of picture quality. Experiments show that the proposed algorithm is feasible and effective, which solves the problem that the traditional method only obtains the binary classification of aesthetic. And this method gives the objective quantization score of the image. At the same time, the algorithm can get the evaluation value which is consistent with the actual situation in the real-time aerial experiment.

Paper Details

Date Published: 31 January 2020
PDF: 8 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114271W (31 January 2020); doi: 10.1117/12.2551979
Show Author Affiliations
Shaoshuo Mu, Zhejiang Univ. of Media and Communications (China)
Yanbing Jiang, Zhejiang Univ. of Media and Communications (China)


Published in SPIE Proceedings Vol. 11427:
Second Target Recognition and Artificial Intelligence Summit Forum
Tianran Wang; Tianyou Chai; Huitao Fan; Qifeng Yu, Editor(s)

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