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Automated Classification of Quilt Photographs Into Crazy and Non- crazy

机译：将被子照片自动分类为疯狂和非疯狂

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摘要

This work addresses the problem of automatic classification and labeling of 19th- and 20th-century quilts from photographs. The photographs are classified according to the quilt patterns into crazy and non - crazy categories. Based on the classification labels, humanists try to understand the distinct characteristics of an individual quilt-maker or relevant quilt-making groups in terms of their choices of pattern selection, color choices, layout, and original deviations from traditional patterns. While manual assignment of crazy and non-crazy labels can be achieved by visual inspection, there does not currently exist a clear definition of the level of crazy-ness, nor an automated method for classifying patterns as crazy and non-crazy. We approach the problem by modeling the level of crazy-ness by the distribution of clusters of color-homogeneous connected image segments of similar shapes. First, we extract signatures (a set of features) of quilt images that represent our model of crazy-ness. Next, we use a supervised classification method, such as the Support Vector Machine (SVM) with the radial basis function, to train and test the SVM model. Finally, the SVM model is optimized using N-fold cross validation and the classification accuracy is reported over a set of 39 quilt images.
机译：这项工作解决了从照片中自动对19世纪和20世纪被子进行分类和标记的问题。这些照片根据被子的图案分为疯狂和非疯狂两类。基于分类标签，人文主义者试图根据图案选择，颜色选择，布局以及与传统图案的原始偏差来了解单个被子或相关被子组的独特特征。尽管可以通过肉眼检查来手动分配疯狂标签和非疯狂标签，但是目前还没有清晰定义疯狂程度的方法，也没有自动的方法将模式分类为疯狂标签和非疯狂标签。我们通过用相似形状的颜色均匀连接图像段的簇的分布来模拟疯狂程度来解决这个问题。首先，我们提取代表我们疯狂模型的被子图像的签名（一组功能）。接下来，我们使用监督分类方法，例如具有径向基函数的支持向量机（SVM），来训练和测试SVM模型。最后，使用N折交叉验证对SVM模型进行优化，并在39个被子图像集上报告分类准确性。

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