A Large-Scale Database of Images and Captions for Automatic Face Naming
A Large-Scale Database of Images and Captions for Automatic Face Naming
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DOI：
10.5244/c.25.29
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
M. Özcan;Jie Luo;V. Ferrari;B. Caputo
中科院分区：
文献类型：
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作者：
M. Özcan;Jie Luo;V. Ferrari;B. Caputo
我们提出了一个大规模的图像和字幕数据库，旨在支持研究如何使用字幕图像从Web上训练视觉分类器。它由从网络上下载的来自不同领域的名人的125，000多张图像组成。每个图像都与其原始文本标题相关联，这些文本标题是从图像来源的html页面中提取的。FAN-Large是Face And Names Large的缩写。据我们所知，它的大小和故意的高噪音水平使其成为支持这类研究的最大和最现实的数据库。数据集及其注释是公开的，可以从http://www.vision获得。ee.ethz.ch/~calvin/fanlarge/.我们报告的结果进行了彻底的评估FAN大使用几种现有的方法的名称-面孔的关联，并提出和评估新的上下文功能来自标题。我们的研究结果提供了关于现有方法的优势和局限性的重要线索。
We present a large scale database of images and captions, designed for supporting research on how to use captioned images from the Web for training visual classifiers. It consists of more than 125,000 images of celebrities from different fields downloaded from the Web. Each image is associated to its original text caption, extracted from the html page the image comes from. We coin it FAN-Large, for Face And Names Large scale database. Its size and deliberate high level of noise makes it to our knowledge the largest and most realistic database supporting this type of research. The dataset and its annotations are publicly available and can be obtained from http://www.vision. ee.ethz.ch/~calvin/fanlarge/. We report results on a thorough assessment of FAN-Large using several existing approaches for name-face association, and present and evaluate new contextual features derived from the caption. Our findings provide important cues on the strengths and limitations of existing approaches.