从真实世界的图像中学习颜色名称

Joost van de Weijer, C. Schmid, J. Verbeek
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引用次数: 199

摘要

在计算机视觉上下文中,颜色命名是为图像像素分配语言颜色标签的动作。一般来说,对颜色命名的研究采用以下范式:在一个定义良好的实验设置中,由多个测试对象标记一组颜色芯片的颜色名称。收集的数据集随后用于在真实图像中标记具有颜色名称的RGB值。除了这个收集过程很耗时这一事实外,还不清楚受控设置中的颜色命名在多大程度上代表了真实图像中的颜色命名。因此,我们建议从现实世界的图像中学习颜色名称。此外,我们通过使用Google Image收集数据集来避免测试对象。由于谷歌图像的限制,该数据集包含大量错误标记的数据。颜色名称是使用适用于此任务的PLSA模型学习的。实验结果表明,从真实图像中学习到的颜色名称在检索和分类上明显优于从标记的颜色芯片中学习到的颜色名称。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning Color Names from Real-World Images
Within a computer vision context color naming is the action of assigning linguistic color labels to image pixels. In general, research on color naming applies the following paradigm: a collection of color chips is labelled with color names within a well-defined experimental setup by multiple test subjects. The collected data set is subsequently used to label RGB values in real-world images with a color name. Apart from the fact that this collection process is time consuming, it is unclear to what extent color naming within a controlled setup is representative for color naming in real-world images. Therefore we propose to learn color names from real-world images. Furthermore, we avoid test subjects by using Google Image to collect a data set. Due to limitations of Google Image this data set contains a substantial quantity of wrongly labelled data. The color names are learned using a PLSA model adapted to this task. Experimental results show that color names learned from real-world images significantly outperform color names learned from labelled color chips on retrieval and classification.
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