Learning Color Names for Real-World Applications
Learning Color Names for Real-World Applications
复制标题
DOI：
10.1109/tip.2009.2019809
复制
发表时间：
2009-07-01
影响因子：
10.6
通讯作者：
Larlus, Diane
中科院分区：
文献类型：
--
作者：
van de Weijer, Joost;Schmid, Cordelia;Larlus, Diane
颜色名称在现实世界的应用程序中是必需的，例如图像检索和图像注释。传统上，它们是从一组标记的彩色芯片中学习的。这些彩色芯片被人类测试对象在一个定义良好的实验设置中标记为颜色名称。然而，在真实世界的图像中命名颜色与这个实验设置有很大的不同。在本文中，我们研究了从颜色芯片中学习到的颜色名称与从现实世界图像中学习到的颜色名称的比较。为了避免用颜色名称手动标记真实世界的图像，我们使用谷歌Image来收集数据集。由于谷歌Image的限制，该数据集包含大量错误标记的数据。我们提出了几种PLSA模型的变体来从这些噪声数据中学习颜色名称。实验结果表明，从真实图像中学习到的颜色名称在图像检索和图像标注方面都明显优于从标记颜色芯片中学习到的颜色名称。
Color names are required in real-world applications such as image retrieval and image annotation. Traditionally, they are learned from a collection of labeled color chips. These color chips are labeled with color names within a well-defined experimental setup by human test subjects. However, naming colors in real-world images differs significantly from this experimental setting. In this paper, we investigate how color names learned from color chips compare to color names learned from real-world images. To avoid hand labeling real-world images with color names, we use Google Image to collect a data set. Due to the limitations of Google Image, this data set contains a substantial quantity of wrongly labeled data. We propose several variants of the PLSA model to learn color names from this noisy data. Experimental results show that color names learned from real-world images significantly outperform color names learned from labeled color chips for both image retrieval and image annotation.