The objective assessment of aesthetic using improved convolution neural network

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.