The DOI matches its Crossref record; 91 of the 91 references it cites were found in a registry or an index.
A Survey on Visual Content-Based Video Indexing and Retrieval
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.
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Large-Scale Concept Ontology for Multimedia
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Affective video content representation and modeling
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Metadata
- Authors
- Hu, W., Xie, N., Li, L., Zeng, X., & Maybank, S.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)
- Published
- 2011-11
- Volume
- 41
- Issue
- 6
- Pages
- 797-819
- ISSN
- 1094-6977 (ISSN-L)
- 1558-2442
- OpenAlex
- W2099736636
- Cited by
- 585
- References
- 91
- FWCI
- 45.89
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
