论文标题
深度学习时代基于内容的图像检索和语义差距
Content-based Image Retrieval and the Semantic Gap in the Deep Learning Era
论文作者
论文摘要
在过去的十年中，基于内容的图像检索令人惊讶，尤其是为了检索查询图像中描绘的对象的图像的任务。此场景称为实例或对象检索，需要在图像之间匹配细粒度的视觉模式。然而，语义并不发挥关键作用。这引起了一个问题：在实例检索中的最新进展是否转移到更通用的图像检索方案？为了回答这个问题，我们首先简要概述了实例检索的最相关里程碑。然后，我们将它们应用于语义图像检索任务，并发现它们在需要图像理解的设置中，其性能较低，并且不那么复杂，更通用的方法。在此之后，我们回顾了通过整合先前的世界知识来缩小这种所谓的语义差距的现有方法。我们得出的结论是，语义图像检索的进一步发展的关键问题在于缺乏标准化的任务定义和适当的基准数据集。
Content-based image retrieval has seen astonishing progress over the past decade, especially for the task of retrieving images of the same object that is depicted in the query image. This scenario is called instance or object retrieval and requires matching fine-grained visual patterns between images. Semantics, however, do not play a crucial role. This brings rise to the question: Do the recent advances in instance retrieval transfer to more generic image retrieval scenarios? To answer this question, we first provide a brief overview of the most relevant milestones of instance retrieval. We then apply them to a semantic image retrieval task and find that they perform inferior to much less sophisticated and more generic methods in a setting that requires image understanding. Following this, we review existing approaches to closing this so-called semantic gap by integrating prior world knowledge. We conclude that the key problem for the further advancement of semantic image retrieval lies in the lack of a standardized task definition and an appropriate benchmark dataset.
