By Yiannis Kompatsiaris, Bernard Merialdo, Shiguo Lian
The fast development of electronic multimedia applied sciences has not just revolutionized the creation and distribution of audiovisual content material, but in addition created the necessity to successfully study television courses to let purposes for content material managers and shoppers. Leaving no stone unturned, TV content material research: ideas and Applications presents a close exploration of television software research techniques.
Leading researchers and teachers from worldwide provide scientifically sound therapy of modern advancements around the comparable topic areas—including platforms, architectures, algorithms, functions, examine effects, rising ways, and open matters. The publication is equipped into six elements:
- Content Extraction - deals with automated research and annotation of television content material, addressing commonplace semantics and ideas in addition to television content
- Content Structuring - examines suggestions for settling on attention-grabbing elements of television courses and delivering direct entry to it
- Content suggestion - explores the matter of delivering clients with the main appropriate content material, addressing the matter of an ever-increasing volume of obtainable content material
- Content caliber - considers visible belief and caliber methods within the multi-display television context and the categorical cellular television scenario
- Web and Social television - presents reviews on internet and television convergence and on how user-generated content material in internet 2.0 functions can be utilized to augment services
- Content construction - covers postproduction, visible results, and presentation standards
Most components begin with a bankruptcy that gives an summary of that quarter, by means of state of the art methods targeting particular concerns coated in that part. Reporting on fresh advances within the box, the publication provide you with the worldwide view and up to date knowing of rising tendencies had to perform the advance of the electronic television domain.
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Additional resources for TV Content Analysis: Techniques and Applications
Joly, O. Buisson, and C. Frelicot. Content-based copy retrieval using distortionbased probabilistic similarity search. IEEE Transactions on Multimedia, 9(2):293–306, 2007. 11. B. Jonsson, H. Lejsek, and L. Amsaleg. The eﬀ2 project: Towards eﬃcient and eﬀective support for large-scale high-dimensional indexing. In International Workshop on Content-Based Multimedia Indexing, 2007 (CBMI ’07), Lisboa, Portugal, pp. 1–10, June 2007. 12. T. Kadir and M. Brady. Scale, saliency and image description. International Journal of Computer Vision, 45(2):83–105, 2001.
5 Experiments: Cross-Domain Learning in TV Programs . . . . . . . . . . 6 Experiments: From TV Programs to Consumer Videos . . . . . . . . . . 1 AS3 VM for Semantic Concept Detection. . . . . . . . . . . . . . 2 Prediction-Based Method for Concept Detection in Events. . . . 7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . .
3 Classiﬁcation with ELFs . . . . . . . . . . . . . . . . . . 5 Experiments: Cross-Domain Learning in TV Programs . . . . . . . . . . 6 Experiments: From TV Programs to Consumer Videos . . . . . . . . . . 1 AS3 VM for Semantic Concept Detection. . . . . . . . . . . . . . 2 Prediction-Based Method for Concept Detection in Events. . . . 7 Conclusion . . . . . . . . . . . . . . . . . . . . . .