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Bridging the semantic gap in sports video retrieval and summarization

机译：弥补运动视频检索和总结中的语义鸿沟

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摘要

One of the major challenges facing current media management systems and related applications is the so-called "semantic gap" between the rich meaning that a user desires and the shallowness of the content descriptions that are automatically extracted from the media. In this paper, we address the problem of bridging this gap in the sports domain. We propose a general framework for indexing and summarizing sports broadcast programs, with a high-level model of sports broadcast video using the concept of an event, defined according to domain-specific knowledge for different types of sports. Within this general framework, we develop automatic event detection algorithms that are based on automatic analysis of the visual and aural signals in the media. We have successfully applied the event detection algorithms to different types of sports including American football, baseball, Japanese sumo wrestling, and soccer. Event modeling and detection contribute to the reduction of the semantic gap by providing rudimentary semantic information obtained through media analysis. We further propose a novel approach, which makes use of independently generated rich textual metadata, to fill the gap completely through synchronization of the information-laden textual data with the basic event segments. We implemented an MPEG-7 compliant browsing system for semantic retrieval and summarization of sports video using the proposed algorithms.
机译：当前媒体管理系统和相关应用程序面临的主要挑战之一是用户期望的丰富含义与从媒体中自动提取的内容描述的浅薄程度之间的所谓“语义鸿沟”。在本文中，我们解决了缩小体育领域差距的问题。我们提出了一个用于对体育广播节目进行索引和汇总的通用框架，其中使用了事件概念的体育广播视频的高级模型，该模型根据针对不同体育项目的领域特定知识进行定义。在此通用框架内，我们开发了基于对媒体中视觉和听觉信号的自动分析为基础的自动事件检测算法。我们已将事件检测算法成功应用于各种类型的运动，包括美式橄榄球，棒球，日本相扑摔跤和足球。事件建模和检测通过提供通过媒体分析获得的基本语义信息，有助于减少语义鸿沟。我们进一步提出了一种新颖的方法，该方法利用独立生成的丰富文本元数据，通过将信息丰富的文本数据与基本事件片段同步来完全填补空白。我们使用提出的算法为体育视频的语义检索和摘要实现了MPEG-7兼容的浏览系统。

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