The art of oil painting reflects on society in the form of vision, while technology constantly explores and provides powerful possibilities to transform the society, which also includes the revolution in the way of art creation and even the way of thinking. The progress of science and technology often provides great changes for the creation of art, and also often changes people's way of appreciation and ideas. The oil painting image feature extraction and recognition is an important field in computer vision, which is widely used in video surveillance, human-computer interaction, sign language recognition and medical, health care. In the past few decades, feature extraction and recognition have focused on the multi-feature fusion method. However, the captured oil painting image is sensitive to light changes and background noise, which limits the robustness of feature extraction and recognition. Oil painting feature extraction is the basis of feature classification. Feature classification based on a single feature is easily affected by the inaccurate detection accuracy of the object area, object angle, scale change, noise interference and other factors, resulting in the reduction of classification accuracy. Therefore, we propose a novel multi-feature fusion method in merging information of heterogenous-view data for oil painting image feature extraction and recognition in this paper. It fuses the width-to-height ratio feature, rotation invariant uniform local binary mode feature and SIFT feature. Meanwhile, we adopt a modified faster RCNN to extract the semantic feature of oil painting. Then the feature is classified based on the support vector machine and K-nearest neighbor method. The experiment results show that the feature extraction method based on multi-feature fusion can significantly improve the average classification accuracy of oil painting and have high recognition efficiency.

中文翻译：

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一种新的多特征融合方法用于油画图像特征提取和识别的异构视图数据融合

油画艺术以视觉的形式反映社会，而技术不断探索，提供了改造社会的强大可能性，其中也包括艺术创作方式乃至思维方式的革命。科学技术的进步，往往为艺术创作带来巨大的变化，也往往会改变人们的欣赏方式和观念。油画图像特征提取与识别是计算机视觉中的一个重要领域，广泛应用于视频监控、人机交互、手语识别和医疗、保健等领域。在过去的几十年里，特征提取和识别主要集中在多特征融合方法上。但是，拍摄的油画图像对光线变化和背景噪声很敏感，这限制了特征提取和识别的鲁棒性。油画特征提取是特征分类的基础。基于单一特征的特征分类容易受到物体区域、物体角度、尺度变化、噪声干扰等因素检测精度不准确的影响，导致分类精度下降。因此，我们提出了一种新的多特征融合方法，用于融合异构视图数据的信息，用于油画图像特征提取和识别。它融合了宽高比特征、旋转不变均匀局部二元模式特征和SIFT特征。同时，我们采用改进的faster RCNN来提取油画的语义特征。然后基于支持向量机和K-近邻方法对特征进行分类。实验结果表明，基于多特征融合的特征提取方法能够显着提高油画的平均分类准确率，具有较高的识别效率。