Image Statistics for Clustering Paintings According to their Visual Appearance
Image Statistics for Clustering Paintings According to their Visual Appearance
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DOI：
10.2312/compaesth/compaesth09/057-064
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发表时间：
2009-05
期刊：
影响因子：
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通讯作者：
M. Spehr;C. Wallraven;R. Fleming
中科院分区：
文献类型：
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作者：
M. Spehr;C. Wallraven;R. Fleming
未经训练的观察者很容易将不同艺术时期的绘画根据其整体视觉外观或“外观”分为不同的组[WCF08]。这些聚类通常受到绘画内容（例如肖像、风景、静物等）的影响，和风格考虑（例如哥特式绘画的“扁平”外观，或野兽派作品中独特的色彩运用）。在这里，我们的目标是确定一组图像测量，可以捕捉这种“天真的艺术视觉印象”，并使用这些功能自动聚类成基于外观的组，就像一个未经训练的观察者的绘画图像的数据库。我们结合了联合收割机广泛的功能，从简单的颜色统计，通过中级空间功能，以高层次的属性，如面部检测算法的输出，这是为了与语义内容。这些特征结合在一起，产生了一组看起来彼此相似的图像，尽管历史时期和内容有所不同。此外，我们测试了性能的特征库在几个分类任务产生良好的效果。我们的工作可以作为一个策展或研究援助，也提供了洞察图像属性，未经训练的科目可能会参加时，判断艺术作品。
Untrained observers readily cluster paintings from different art periods into distinct groups according to their overall visual appearance or 'look' [WCF08]. These clusters are typically influenced by both the content of the paintings (e.g. portrait, landscape, still-life, etc.), and stylistic considerations (e.g. the 'flat' appearance of Gothic paintings, or the distinctive use of colour in Fauve works). Here we aim to identify a set of image measurements that can capture this 'naive visual impression of art', and use these features to automatically cluster a database of images of paintings into appearance-based groups, much like an untrained observer. We combine a wide range of features from simple colour statistics, through mid-level spatial features to high-level properties, such as the output of face-detection algorithms, which are intended to correlate with semantic content. Together these features yield clusters of images that look similar to one another despite differences in historical period and content. In addition, we tested the performance of the feature library in several classification tasks yielding good results. Our work could be applied as a curatorial or research aid, and also provides insight into the image attributes that untrained subjects may attend to when judging works of art.