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Recognition of Chinese artists via windowed and entropy balanced fusion in classification of their authored ink and wash paintings (IWPs)

机译:通过窗口和熵平衡融合对中国艺术家的水墨画(IWP)分类进行识别

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摘要

As one of the most important cultural heritages, ink and wash paintings (IWPs) play an important role in the world of traditional Chinese arts. In comparison with western arts, the Chinese IWPs have the unique feature that the art form is primarily populated with limited number of content elements, such as stones, mountains, flowers, and animals etc. and hence most likely different art pieces share similar content, making it difficult to differentiate in terms of content alone. In this paper, we propose to extract histogram-based local feature and global feature to characterize different aspects of art styles, and such features are applied to drive neural networks to complete the classification of IWPs in terms of individual artistic descriptors. We then propose a windowed and entropy balanced fusion scheme to make integrated decisions to optimize the final classification and recognition results. Extensive evaluation via experiments is also reported, which supports that the proposed algorithm achieves good performances, outperforming the existing benchmark techniques and hence providing an excellent potential for computerized analysis and management of traditional Chinese IWPs.
机译:作为最重要的文化遗产之一,水墨画(IWP)在中国传统艺术世界中发挥着重要作用。与西方艺术相比,中国的IWP具有的独特之处在于,艺术形式主要是由数量有限的内容元素组成的,例如石头,山脉,花卉和动物等,因此,很可能不同的艺术品共享相似的内容,仅凭内容就很难区分。在本文中,我们建议提取基于直方图的局部特征和全局特征来表征艺术风格的不同方面,并将这些特征应用于驱动神经网络以根据单个艺术描述符完成IWP的分类。然后,我们提出了一种窗口化和熵平衡的融合方案,以做出综合决策,以优化最终的分类和识别结果。还报告了通过实验进行的广泛评估,这证明了所提出的算法具有良好的性能,优于现有的基准技术,因此为中国传统IWP的计算机分析和管理提供了极好的潜力。

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