The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Concept-Based Video Retrieval
Abstract
Concept-Based Video Retrieval reviews 300 references on video retrieval, indicating when the text-only solutions of present-day video search engines are unsatisfactory and showing the promising alternatives which are primarily concept-based. Central to the discussion, therefore, is the fundamental notion of a semantic concept: an objective linguistic description of an observable entity. The book aims to motivate and explain how automated detection, selection under uncertainty, and interactive usage might solve the major scientific problems for video retrieval: the semantic gap. In striving to bridge this gap, the authors structured their review by laying down the anatomy of a concept-based video search engine. They present a component-wise decomposition and evaluation of such an interdisciplinary multimedia system, covering influences from information retrieval, computer vision, machine learning, and human-computer interaction. Concept-Based Video Retrieval is aimed primarily at researchers and developers in the broad area of information retrieval. It will also be an invaluable reference for students in computer and information science at the (post)graduate level, as well as industrial practitioners
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Speeded-Up Robust Features (SURF)
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557
Introduction to information retrieval
(2009) · Choice Reviews Online · doi:10.5860/choice.46-2715 · Cited by 12,278
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
Optimal multimodal fusion for multimedia data analysis
Wu, Y., Chang, E. Y., Chang, K. C.-C., & Smith, J. R. (2004) · Proceedings of the 12th annual ACM international conference on Multimedia · doi:10.1145/1027527.1027665 · Cited by 229
Cited by
Large-scale visual sentiment ontology and detectors using adjective noun pairs
Borth, D., Ji, R., Chen, T., Breuel, T., & Chang, S.-F. (2013) · Proceedings of the 21st ACM international conference on Multimedia · doi:10.1145/2502081.2502282 · Cited by 851
LifeLogging: Personal Big Data
Gurrin, C., Smeaton, A. F., & Doherty, A. R. (2014) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000033 · Cited by 434
Event-based media processing and analysis: A survey of the literature
Tzelepis, C., Ma, Z., Mezaris, V., Ionescu, B., Kompatsiaris, I., Boato, G., Sebe, N., & Yan, S. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.05.005 · Cited by 49
Shirahama, K., & Grzegorzek, M. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2292-8 · Cited by 24
Metadata
- Authors
- Snoek, C. G. M., & Worring, M.
- Venue
- now publishers, Inc. eBooks
- Publisher
- now Publishers Inc
- Published
- 2007
- OpenAlex
- W2137117795
- Cited by
- 317
- References
- 8
- FWCI
- 9.04
- Type
- book
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Concept-Based Video Retrieval. https://app.cochat.ai/papers/10.1561/9781601982353
- The paper
- Snoek, C. G. M., & Worring, M. (2007). Concept-Based Video Retrieval. now publishers, Inc. eBooks. https://doi.org/10.1561/9781601982353
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
