人像照片的美感质量评价

随着机器学习理论和图形图像处理技术的不断发展,在计算机视觉和计算美学领域中人们越来越关注如何建立自动评价和判断图片美感质量的系统.该系统将可用来补充和完善原有对照片只有主观美感质量评价的这一问题.对人像照片美感质量的客观评价进行研究,提出了25种能够较好反映人像类照片美感质量的特征,并使用支持向量机、Adaboost、随机森林等多种分类器来进行机器学习和评价,对提出的特征值集合进行十交叉检验并探讨了哪些特征对美感评价有较强影响等问题.最后,通过与现有研究结果进行对比分析后得出,当采用所提出的25种特征进行人像照片美感质量评价和分类时有更高的准确率,即使用于机器学习的训练数据集数目较少时仍能保持较高的准确率.

Evaluating Aesthetics Quality in Portrait Photos

With the development of machine learning theory and image processing technology, peoples are more and more interested in how to build a system to automatically evaluate and assess aesthetics quality of photos in the field of computer vision and computational aesthetics. This type of system can be a supplement for the subjective assessment of photo aesthetic quality. In this paper, 25 visual features extracted from each image are used to objectively evaluate photo aesthetic quality, which can better reflect the aesthetic quality of portrait photo. Four aesthetics classifiers are built based on support vector machine, AdaBoost, random forest and linear regression. 10-Fold cross validation experiment is performed to reveal which features have a salient impact on the aesthetic assessment. Compared to the current research results, classifiers using 25 features proposed by this study have higher classification accuracy rate for portrait photo aesthetic evaluation, even using the smaller training sets.