In this paper, we review 300 references on video retrieval, indicating when text-only solutions are unsatisfactory and showing the promising alternatives which are in majority concept-based. Therefore, central to our discussion is the notion of a semantic concept: an objective linguistic description of an observable entity. Specifically, we present our view on how its automated detection, selection under uncertainty, and interactive usage might solve the major scientific problem for video retrieval: the semantic gap. To bridge the gap, we lay down the anatomy of a concept-based video search engine. We present a component-wise decomposition of such an interdisciplinary multimedia system, covering influences from information retrieval, computer vision, machine learning, and human–computer interaction. For each of the components we review state-of-the-art solutions in the literature, each having different characteristics and merits. Because of these differences, we cannot understand the progress in video retrieval without serious evaluation efforts such as carried out in the NIST TRECVID benchmark. We discuss its data, tasks, results, and the many derived community initiatives in creating annotations and baselines for repeatable experiments. We conclude with our perspective on future challenges and opportunities.

在本文中，我们回顾了300篇有关视频检索的参考文献，指出纯文本解决方案何时不尽人意，并显示了大多数基于概念的有前途的替代方案。因此，我们讨论的中心是语义概念的概念：可观察实体的客观语言描述。具体而言，我们就其自动检测，不确定性下的选择以及交互式用法如何解决视频检索的主要科学问题：语义鸿沟提出了看法。为了缩小差距，我们确定了基于概念的视频搜索引擎的结构。我们介绍了这种跨学科的多媒体系统的组件分解，涵盖了信息检索，计算机视觉，机器学习和人机交互的影响。对于每个组件，我们都会回顾文献中的最新解决方案，每个解决方案都具有不同的特征和优点。由于这些差异，如果没有认真的评估工作（例如在NIST TRECVID基准测试中进行），我们将无法理解视频检索的进展。我们将讨论其数据，任务，结果以及许多派生的社区活动，以创建可重复实验的注释和基准。我们以对未来挑战和机遇的看法作为结束。以及为衍生可重复实验创建注释和基准的许多社区活动。我们以对未来挑战和机遇的看法作为结束。以及为衍生可重复实验创建注释和基准的许多社区活动。我们以对未来挑战和机遇的看法作为结束。