Personalized Abstraction of Broadcasted American Football Video by Highlight Selection

Babaguchi, Noboru; Kawai, Yoshihiro; Ogura, T.; Kitahashi, Tadahiro

doi:10.1109/tmm.2004.830811

Cited by 109 publications

(83 citation statements)

References 3 publications

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“…Babaguchi et al [2] propose an idea to define the significance degrees of events for American football, while they perform game summarization from existing text-based game logs rather than the results of automatic event detection. We follow similar ideas and modify the definition of significance especially for baseball videos.…”

Section: Significance Degree Of Eventsmentioning

confidence: 99%

Explicit semantic events detection and development of realistic applications for broadcasting baseball videos

Chu

2007

Multimed Tools Appl

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This paper presents a framework that explicitly detects events in broadcasting baseball videos and facilitates the development of many practical applications. Three phases of contributions are included in this work: reliable shot classification, explicit event detection, and elaborate applications. At the shot classification stage, color and geometric information are utilized to classify video shots into several canonical views. To explicitly detect semantic events, rule-based decision and model-based decision methods are developed. We emphasize that this system efficiently and exactly identifies what happened in baseball games rather than roughly finding some interesting parts. On the basis of explicit event detection, many accurate and practical applications such as automatic box score generation and game summarization could be built. The reported results show the effectiveness of the proposed framework and demonstrate some research opportunities about bridging the semantic gap for sports videos.

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Section: Significance Degree Of Eventsmentioning

confidence: 99%

Explicit semantic events detection and development of realistic applications for broadcasting baseball videos

Chu

2007

Multimed Tools Appl

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“…As an important genre of digital video documents, sports video has attracted increasing attention in automatic video analysis [1,2,3,4,5,6,7,8] due to its wide viewership and tremendous commercial potential. Most of existing work has been extensively studied on the content extraction of highlight summary [3,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Highlight Ranking for Broadcast Tennis Video Based on Multi-modality Analysis and Relevance Feedback

Zhu

Huang

Gong³

2008

Advances in Multimedia Information Processing - PCM 2008

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Abstract. Most of existing work on sports video analysis concentrates on highlight extraction. Few efforts devoted to the important issue as how to organize the extracted highlights which is adapt for the user preference. In this paper, we propose a novel approach to rank the highlights extracted from broadcast tennis video based on multi-modality analysis and relevance feedback. Firstly, visual and auditory features are employed to construct the mid-level representations for the content of broadcast tennis video. Then, the affective features are extracted from mid-level representations and the multiple ranking models are built using nonlinear regression algorithm. Finally, the ranking models are linearly combined to generate the final highlight ranking results. The relevance feedback technique is employed to effectively capture the user interest in visual and auditory attention spaces to adjust the ranking results being suitable to the user preference. The experimental results are encouraging and demonstrate that our approach is effective.

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“…Satoh et al [16] have developed a system that identifies faces, by associating the faces extracted from the visual stream and their names from the streams of CC text and video captions. Babaguchi et al [8] tried exploiting graphical overlays including text to find highlights in the video.…”

Section: Related Workmentioning

confidence: 99%

“…As a result, we detected all the event frames when the overlays showing the event time were really present. The above procedure is introduced to highlight based video abstraction [8]. This approach seems somewhat limited, but is vital for the broadcasted sports video with the graphics stream if the external metadata is available.…”

Section: Collaboration Between Graphics Stream and External Metadatamentioning

confidence: 99%

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Intermodal collaboration: a strategy for semantic content analysis for broadcasted sports video

Babaguchi

Nitta

Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429)

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This paper presents intermodal collaboration: a strategy for semantic content analysis for broadcasted sports video. The broadcasted video can be viewed as a set of multimodal streams such as visual, auditory, text (closed caption) and graphics streams. Collaborative analysis for the multimodal streams is achieved based on temporal dependency between their streams, in order to improve the reliability and efficiency for semantic content analysis such as extracting highlight scenes from sports video and automatically generating annotations of specific scenes. A couple of case studies are shown to experimentally confirm the effectiveness of intermodal collaboration.

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Personalized Abstraction of Broadcasted American Football Video by Highlight Selection

Cited by 109 publications

References 3 publications

Explicit semantic events detection and development of realistic applications for broadcasting baseball videos

Explicit semantic events detection and development of realistic applications for broadcasting baseball videos

Highlight Ranking for Broadcast Tennis Video Based on Multi-modality Analysis and Relevance Feedback

Intermodal collaboration: a strategy for semantic content analysis for broadcasted sports video

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