一种用户偏好的美学图像推荐方法

User-specific method for aesthetic images recommendation

在众多的图像信息资源中快速、有效地寻找用户最喜欢的图像,提出了一种用户偏好的美学图像推荐方法,通过使用深度卷积神经网络提取图像的深层特征,并经过SVMrank后得到一个图像排序得分,同时使用手工标记的图像美学因素(如色调法、图像组合规则、清晰度以及简洁性)计算并得到图像的美学特征,得到一个美学得分,最后进行加权交叉验证得到一个令用户满意的推荐结果。通过实验表明该算法为一种有效的美学偏好推荐方法。

To quickly and effectively find the user’s favorite image in many image information resources,this paper proposed a user-appreciated aesthetic image recommendation method,which used the deep convolutional neural network to extract the deep features of the image,and obtained an image sorting score after SVMrank,while using hand-marked image aesthetic factors( such as hue method,image combination rule,definition and simplicity) calculated and obtained the aesthetic characteristics of the image and an aesthetic score. Finally it performed weighted cross-validation to obtain a recommendation result that was satisfactory to the user. Experiments show that the algorithm is an effective recommendation method for aesthetic preferences.