Abstract: In recent years, content-based retrieval and classification of nature images is widely studied, most of which take advantage of color component. Therefore, such methods are not suitable for IWPs analysis. In this paper, after image pre-processing, grayscale histogram is applied to describing the intensity distribution and compositional style. Based on Back-Propagation（BP） neural networks, Chinese arts are classified by observation and analysis. The experimental results prove that it is feasible to extract global feature based on histogram, and it can achieve high accuracy of automated classification.

Key words: Ink and Wash Paintings（IWPs）, feature extraction, BP neural networks, histogram

摘要： 基于内容的照片或自然图像分析和检索已经得到广泛的研究，但多数都基于颜色特征，不能直接用于水墨画艺术作品的分析。对图像进行预处理，提出基于灰度直方图描述水墨画笔道力度分布信息及构图风格，并利用神经网络学习水墨画整体技法风格特征，依据此特征进行不同艺术家间的分类。实验结果表明，通过对整体风格特征的提取，直方图模型能够有效表征画家不同的艺术风格，实现了水墨画的自动分类。

关键词: 水墨画, 特征提取, BP神经网络, 直方图