Recent years have witnessed the dramatic evolution in visual data volume and processing capabilities. For example, technical advances have enabled 3D modeling from large-scale crowdsourced photo collections. Compared to static image datasets, exploration and exploitation of Internet video collections are still largely unsolved. To address this challenge, we first propose to represent video contents using a histogram representation of iconic imagery attained from relevant visual datasets. We then develop a data-driven framework for a fully unsupervised extraction of such representations. Our novel Bag-of-Iconics (BoI) representation efficiently analyzes individual videos within a large-scale video collection. We demonstrate our proposed BoI representation with two novel applications: (1) finding video sequences connecting adjacent landmarks and aligning reconstructed 3D models and (2) retrieving geometrically relevant clips from video collections. Results on crowdsourced datasets illustrate the efficiency and effectiveness of our proposed Bag-of-Iconics representation.

中文翻译：

---

使用了解几何的图标袋表示法进行有效的视频收集关联

近年来，目睹了视觉数据量和处理能力的巨大发展。例如，技术进步已使大规模众包照片集的3D建模成为可能。与静态图像数据集相比，对Internet视频集的探索和开发仍未解决。为了解决这一挑战，我们首先建议使用从相关视觉数据集获得的图标图像的直方图表示来表示视频内容。然后，我们开发了一个数据驱动的框架，用于完全无监督地提取此类表示。我们新颖的图标袋（BoI）表示可有效分析大型视频集合中的单个视频。我们通过两个新颖的应用程序展示了我们提出的BoI表示形式：（1）找到连接相邻地标的视频序列，并对齐重建的3D模型，以及（2）从视频集合中检索几何上相关的剪辑。众包数据集上的结果说明了我们提出的图标袋表示法的效率和有效性。