论文标题
抽象图像具有不同级别的每个反向图像搜索引擎的可检索性
Abstract Images Have Different Levels of Retrievability Per Reverse Image Search Engine
论文作者
论文摘要
许多计算机视觉研究集中在自然图像上，但是技术文档通常由抽象图像，例如图表，图纸，图表和原理图。通用的Web搜索引擎如何发现抽象图像？计算机视觉和机器学习的最新进展导致了反向图像搜索引擎的兴起。如果传统搜索引擎接受文本查询并返回一组文档结果，包括图像，反向图像搜索接受图像作为查询，并将一组图像作为结果返回。本文评估了常见的反向图像搜索引擎发现抽象图像的很好。我们进行了一项实验，利用了Wikimedia Commons的图像，Wikimedia Commons是一个已知由Baidu，Bing，Google和Yandex索引的网站。我们衡量图像很难再次找到（可检索性），返回的图像是相关的百分比（精度）以及访问者在找到提交的图像之前必须审查的平均结果数（平均相互等级）。当试图在相似图像中再次发现相同的图像时，Yandex会表现最好。在搜索包含特定图像的页面时，Google和Yandex的表现分别以0.8191至0.8297的精度分数发现照片时胜过其他图像。在这两种情况下，Google和Yandex在自然图像方面的表现要比抽象图像效果更好，而这些图像之间的可检索性差异高达54 \％。这些结果会影响任何应用通用Web搜索引擎的人搜索使用抽象图像的技术文档。
Much computer vision research has focused on natural images, but technical documents typically consist of abstract images, such as charts, drawings, diagrams, and schematics. How well do general web search engines discover abstract images? Recent advancements in computer vision and machine learning have led to the rise of reverse image search engines. Where conventional search engines accept a text query and return a set of document results, including images, a reverse image search accepts an image as a query and returns a set of images as results. This paper evaluates how well common reverse image search engines discover abstract images. We conducted an experiment leveraging images from Wikimedia Commons, a website known to be well indexed by Baidu, Bing, Google, and Yandex. We measure how difficult an image is to find again (retrievability), what percentage of images returned are relevant (precision), and the average number of results a visitor must review before finding the submitted image (mean reciprocal rank). When trying to discover the same image again among similar images, Yandex performs best. When searching for pages containing a specific image, Google and Yandex outperform the others when discovering photographs with precision scores ranging from 0.8191 to 0.8297, respectively. In both of these cases, Google and Yandex perform better with natural images than with abstract ones achieving a difference in retrievability as high as 54\% between images in these categories. These results affect anyone applying common web search engines to search for technical documents that use abstract images.
