Image beauty assessment is an important subject of computer vision. Therefore, building a model to mimic the image beauty assessment becomes an important task. To better imitate the behaviours of the human visual system (HVS), a complete survey about images of different categories should be implemented. This work focuses on image beauty assessment. In this study, the pairwise evaluation method was used, which is based on the Bradley-Terry model. We believe that this method is more accurate than other image rating methods within an image group. Additionally, Convolution neural network (CNN), which is fit for image quality assessment, is used in this work. The first part of this study is a survey about the image beauty comparison of different images. The Bradley-Terry model is used for the calculated scores, which are the target of CNN model. The second part of this work focuses on the results of the image beauty prediction, including landscape images, architecture images and portrait images. The models are pretrained by the AVA dataset to improve the performance later. Then, the CNN model is trained with the surveyed images and corresponding scores. Furthermore, this work compares the results of four CNN base networks, i.e., Alex net, VGG net, Squeeze net and LSiM net, as discussed in literature. In the end, the model is evaluated by the accuracy in pairs, correlation coefficient and relative error calculated by survey results. Satisfactory results are achieved by our proposed methods with about 70 percent accuracy in pairs. Our work sheds more light on the novel image beauty assessment method. While more studies should be conducted, this method is a promising step.
翻译:图像美观评估是计算机视觉的一个重要主题。 因此, 建立模拟图像美观评估的模型将是一项重要任务。 为了更好地模仿人类视觉系统( HVS) 的行为, 应该对不同类别的图像进行全面调查 。 这项工作侧重于图像美观评估 。 在这项研究中, 使用了基于布拉德利- Terry 模型的对称评估方法。 我们认为, 这种方法比图像组中的其他图像评级方法更准确。 此外, 这项工作中使用了适合图像质量评估的 Convolution 神经网络( CNN) 。 这项研究的第一部分是对不同图像的图像准确性比较的调查。 布拉德- Terry 模型用于计算得分, 这是CNNM模型模型模型的目标。 这项工作的第二部分侧重于图像美观预测的结果, 包括景观图像、 建筑图象图像和肖像图像。 模型受AVAVA数据集的预先训练, 来改善业绩。 然后, CNN 模型与调查的图像和相应的分数 。 此外, 这项工作比较了四个CNN 网络基础网络网络网络的准确性网络结果, i. Alex VGS 的计算结果, 通过S est rodeal res rode res res roudal roduc roduc roducal roducal rodudeal be the the the the the compal res res res res res res resal resal res res res resaltial rodudeal rodudududeal rodude rodududude rodudeal rodudeal resal roal rodudeal rodude roal rodudeal rodudeal roal roal roal roal roal roal roal res res robal robal robal robal robal roal roal roal roal robal roal robal roal roal roal roal roal roal roal roal roal roal roal roal roal roal roal