利用图像熵和复杂网络的中国画分类方法

Chinese Painting Classification Method Using Image Entropy and Complex Network

与以题款印鉴为主要依据的人工分类方法不同,采用图像内容特征作为计算机分类的信息来源,是数字化中国画管理的重要工作。针对数字化中国画存在的各种不规范问题和已有特征提取算法的一些不足,提出了基于图像熵的分块筛选方法,与复杂网络理论相结合来提取中国画的纹理特征,并使用支持向量机进行分类。实验结果表明,该方法能有效地提取中国画纹理特征并进行分类,且在图像不规范的情况下依然有较好的表现。

Different from the artificial classification of the inscription and seal, the computer classification uses image content characteristic as a source of information and is a key work in the digital management. Aiming at the non-standard problems and deficiencies of the existing feature extraction algorithms, a segmentation filtering method based on image entropy is proposed. The texture characteristics of traditional Chinese paintings are extracted by the method combined with the complex network theory, and then a support vector machine is used to classify. Experimental results show that this method can effectively extract textural features and categorize Chinese paintings, and it still has a good performance in the case of non-standard images.