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Title
Abstract
FAQs
Figures
Introduction
Video Structure Analysis
Video Data Mining
Future Developments
Conclusion
References
All Topics
Computer Science
Computer Vision

A Survey on Visual Content-Based Video Indexing and Retrieval

Profile image of Stephen MaybankStephen Maybank

2000, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)

https://doi.org/10.1109/TSMCC.2011.2109710
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Abstract

Video indexing and retrieval have a wide spectrum of promising applications, motivating the interest of researchers worldwide. This paper offers a tutorial and an overview of the landscape of general strategies in visual content-based video indexing and retrieval, focusing on methods for video structure analysis, including shot boundary detection, key frame extraction and scene segmentation, extraction of features including static key frame features, object features and motion features, video data mining, video annotation, video retrieval including query interfaces, similarity measure and relevance feedback, and video browsing. Finally, we analyze future research directions.

FAQs

sparkles

AI

What recent methodologies improve shot boundary detection accuracy?add

The paper reveals that adaptive threshold-based algorithms significantly enhance shot boundary detection accuracy, outperforming global threshold methods by incorporating local content variations.

How do recent video classification approaches utilize semantic concepts?add

The research demonstrates that successful video classification increasingly employs machine learning techniques integrated with hierarchical semantic concepts, as showcased by Yuan et al.'s support cluster machines achieving notable accuracy.

What are common limitations in current key frame extraction techniques?add

The various algorithms for key frame extraction often struggle with redundancy and fail to effectively represent dynamic scene content, which restricts their applicability in complex video environments.

How does the integration of audio content enhance video summarization?add

Dynamic video skimming methods that include audio content provide a temporal evolution of the video, making the summary more engaging and informative, as highlighted in recent frameworks for news summarization.

What areas in video retrieval need further research according to this survey?add

Key areas identified for future research include motion feature analysis, hierarchical video indexing, and integrating multimodal human-computer interaction to enhance retrieval accuracy and user experience.

Figures (2)
SURVEYS ON VIDEO INDEXING AND RETRIEVAL
SURVEYS ON VIDEO INDEXING AND RETRIEVAL

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