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首页> 外文期刊>Journal of visual communication & image representation >Artistic movement recognition by consensus of boosted SVM based experts
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Artistic movement recognition by consensus of boosted SVM based experts

机译：通过支持SVM的专家达成共识来认可艺术运动

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In this work we aim to automatically recognize the artistic movement from a digitized image of a painting. Our approach uses a new system that resorts to descriptions induced by color structure histograms and by novel topographical features for texture assessment. The topographical descriptors accumulate information from the first and second local derivatives within four layers of finer representations. The classification is performed by two layers of ensembles. The first is an adapted boosted ensemble of support vector machines, which introduces further randomization over feature categories as a regularization. The training of the ensemble yields individual experts by isolating initially misclassified images and by correcting them in further stages of the process. The solution improves the performance by a second layer build upon the consensus of multiple local experts that analyze different parts of the images. The resulting performance compares favorably with classical solutions and manages to match the ones of modern deep learning frameworks. (C) 2018 Elsevier Inc. All rights reserved.
机译：在这项工作中，我们的目标是从绘画的数字化图像中自动识别艺术运动。我们的方法使用了一种新系统，该系统诉诸于由颜色结构直方图和新颖的地形特征引起的描述以进行纹理评估。地形描述符从四层较细的表示中累积来自第一和第二局部导数的信息。分类由两层合奏执行。第一个是支持向量机的自适应增强集成，它在特征类别上引入了进一步的随机化作为正则化。通过隔离最初错误分类的图像并在过程的进一步阶段对其进行校正，可以训练整体专家。该解决方案在分析图像不同部分的多个本地专家的共识基础上，通过第二层提高了性能。由此产生的性能与经典解决方案相比具有优势，并且可以与现代深度学习框架相匹配。 （C）2018 Elsevier Inc.保留所有权利。

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