Book contents
- Frontmatter
- Contents
- Preface
- Acknowledgments
- 1 Introduction
- 2 Multimedia: A Definition
- 3 Elements of Multimedia Computing
- 4 Introduction to Sensors
- 5 Sound
- 6 Light
- 7 Multimedia Documents
- 8 Multimodal Integration and Synchronization
- 9 Multimedia Systems
- 10 The Human Factor
- 11 Fundamentals of Compression
- 12 Lossy Compression
- 13 Advanced Perceptual Compression
- 14 Speech Compression
- 15 Multimedia Information Retrieval
- 16 Signal Processing Primer
- 17 Multimedia Content Analysis
- 18 Content Analysis Systems
- 19 Content and Context
- 20 Future Topics
- Index
- Plate Section
- References
17 - Multimedia Content Analysis
Published online by Cambridge University Press: 12 September 2018
Book contents
- Frontmatter
- Contents
- Preface
- Acknowledgments
- 1 Introduction
- 2 Multimedia: A Definition
- 3 Elements of Multimedia Computing
- 4 Introduction to Sensors
- 5 Sound
- 6 Light
- 7 Multimedia Documents
- 8 Multimodal Integration and Synchronization
- 9 Multimedia Systems
- 10 The Human Factor
- 11 Fundamentals of Compression
- 12 Lossy Compression
- 13 Advanced Perceptual Compression
- 14 Speech Compression
- 15 Multimedia Information Retrieval
- 16 Signal Processing Primer
- 17 Multimedia Content Analysis
- 18 Content Analysis Systems
- 19 Content and Context
- 20 Future Topics
- Index
- Plate Section
- References
Summary
One of the biggest research questions in multimedia is how to make the computer (partly) understand the content of an image, audio, or video file. The field inside multimedia computing exploring this is called multimedia content analysis. Multimedia content analysis tries to infer information from multimedia data, that is, from images, audio, or video, with the goal of enabling interesting applications.
In contrast to fields like computer vision or natural language processing that adopt a restricted view focusing on only one data type, multimedia content analysis adopts a top-down viewpoint studying how computer systems should be designed to process the different types of information in a document in an integrated fashion, including metadata (for example geo-tags) and the context in which the content is presented (for example the social graph of an author). Many examples have been discussed in various chapters of this book. The top-down approach of multimedia computing mostly succeeds in solving problems with data whose scale and diversity challenge the current theory and practice of computer science. Therefore, progress is measured using empirical methods, often in global competitions and evaluations that use large volumes of data.
Information
- Type
- Chapter
- Information
- Multimedia Computing , pp. 233 - 271Publisher: Cambridge University PressPrint publication year: 2014
References
, , , and . “A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains.” Ann. Math. Statist., 41 (1): 164–71, 1970.CrossRefGoogle Scholar
Kevin Beyer, Jonathan Goldstein, Raghu Ramakrishnan, and Uri Shaft. “When is ‘nearest neighbor’ meaningful?” Int. Conf. on Database Theory. 1999.
and . Applied Optimal Control: Optimization, Estimation, and Control. Blaisdell Publishing Company or Xerox College Publishing. 1969. p. 481.Google Scholar
, , and . Pattern Classification. New York: Wiley Interscience, 2nd edition, p. 680.
and . “Large margin classification using the perceptron algorithm.” Proceedings of the 11th Annual Conference on Computational Learning Theory (COLT ’98). ACM Press. 1998.
. “Figures of merit for assessing connected word recognisers.”
Speech Communication, 9: 239–336, 1990.CrossRefGoogle Scholar
in The Art of Computer Programming, volume 3. 1973.
and . “On information and sufficiency.” Annals of Mathematical Statistics, 22 (1): 79–86, 1951.CrossRef
. “Distinctive image features from scale-invariant keypoints.” International Journal of Computer Vision, 60 (2): 91–110, 2004.
CrossRef
. “Some methods for classification and analysis of multivariate observations.” 1. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press. pp. 281–97, 1967.
and . “Building the gist of a scene: The role of global image features in recognition.” In Progress in Brain Research, vol. 155. Ed. , , and . New York: Elsevier B. V. 2006.Google Scholar
: “Fast training of support vector machines using sequential minimal optimization.” In Advances in Kernel Methods: Support Vector Learning, 185–208. Boston, MA: MIT Press.
1999.Google Scholar
. “The Perceptron: A probabilistic model for information storage and organization in the brain.” Cornell Aeronautical Laboratory, Psychological Review, 65 (6): 386–408, 1958.
, , and . “The earth mover’s distance as a metric for image retrieval.” International Journal of Computer Vision, 40 (2): 99–122, 2000.
and . Artificial Intelligence – A Modern Approach,. Essex, UK: Prentice Hall, 3rd edition, 2009.Google Scholar
. “Error bounds for convolutional codes and an asymptotically optimum decoding algorithm.” IEEE Transactions on Information Theory, 13 (2): 260–9, April 1967.
CrossRef
. “Hierarchical grouping to optimize an objective function.” Journal of the American Statistical Association, 58 (301): 236–44, 1963.CrossRef
and . “30 years of adaptive neural networks: Perceptron, madaline, and backpropagation.” Proc. IEEE, 78 (9): 1415–42, 1990.CrossRefGoogle Scholar
and . Data Mining: Practical Machine Learning Tools and Techniques. Burlington, MA: Morgan Kaufman. 2005.Google Scholar
Accessibility standard: Unknown
Why this information is here
This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.Accessibility Information
Accessibility compliance for the PDF of this chapter is currently unknown
and may be updated in the future.