The increasing availability of extensive digitized fine art collections opens up new research directions. In particular, correctly identifying the artistic style or art movement of paintings is crucial for large artistic database indexing, painter authentication, and mobile recognition of painters. Even though the implementation of CNN on artwork classification improved the performance dramatically compared to tradition classifier, the feature extraction methods are still valuable to help establishing better image representation for both common classifiers and neural networks. The main goal of this article is to present three novel features and a mature model structure for artistic movement recognition of portrait paintings. The proposed features include two unique color features and one texture feature: (a) Modified Color Distance (MCD), (b) ColorRatio Feature and (c) Weber's law Based Texture Feature. We demonstrate the superiority of our proposed method over the state-of-the-art approaches, and how successful our features are to support features from various neural networks. Another contribution of our work is a new portrait database that consists of 927 paintings from 6 different art movements. Extensive computer evaluations on this database show that we achieved an average accuracy of 98% for classifying two categories and 82.6% for classifying all 6 categories. Besides, our novel features improved the performance of pre-trained CNN significantly.

广泛的数字化美术收藏的可用性不断提高，开辟了新的研究方向。尤其是，正确识别绘画的艺术风格或艺术运动对于大型艺术数据库索引，画家认证和画家的移动识别至关重要。尽管与传统分类器相比，CNN在艺术品分类上的实现显着提高了性能，但特征提取方法对于帮助为常见分类器和神经网络建立更好的图像表示仍然很有价值。本文的主要目的是为肖像画的艺术动作识别提供三个新颖的特征和成熟的模型结构。拟议的功能包括两个独特的颜色功能和一个纹理功能：（a）修改的颜色距离（MCD），（b）ColorRatio特征和（c）基于Weber定律的纹理特征。我们证明了我们提出的方法优于最新方法的优越性，以及我们的功能如何成功支持各种神经网络的功能。我们工作的另一个贡献是建立了一个新的肖像数据库，其中包含来自6种不同艺术运动的927幅画作。在该数据库上进行的大量计算机评估表明，我们对两个类别进行分类的平均准确度为98％，而对所有6个类别进行分类的平均准确度为82.6％。此外，我们的新颖功能大大提高了预训练CNN的性能。以及我们的功能如何成功支持各种神经网络的功能。我们工作的另一个贡献是建立了一个新的肖像数据库，其中包含来自6种不同艺术运动的927幅画作。在该数据库上进行的大量计算机评估表明，我们对两个类别进行分类的平均准确度为98％，而对所有6个类别进行分类的平均准确度为82.6％。此外，我们的新颖功能大大提高了预训练CNN的性能。以及我们的功能如何成功支持各种神经网络的功能。我们工作的另一个贡献是建立了一个新的肖像数据库，其中包含来自6种不同艺术运动的927幅画作。在该数据库上进行的大量计算机评估表明，我们对两个类别进行分类的平均准确度为98％，而对所有6个类别进行分类的平均准确度为82.6％。此外，我们的新颖功能大大提高了预训练CNN的性能。