Information Fusion
Machine Learning Methods
(Sprache: Englisch)
In the big data era, increasing information can be extracted from the same source object or scene. For instance, a person can be verified based on their fingerprint, palm print, or iris information, and a given image can be represented by various...
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Klappentext zu „Information Fusion “
In the big data era, increasing information can be extracted from the same source object or scene. For instance, a person can be verified based on their fingerprint, palm print, or iris information, and a given image can be represented by various types of features, including its texture, color, shape, etc. These multiple types of data extracted from a single object are called multi-view, multi-modal or multi-feature data. Many works have demonstrated that the utilization of all available information at multiple abstraction levels (measurements, features, decisions) helps to obtain more complex, reliable and accurate information and to maximize performance in a range of applications.This book provides an overview of information fusion technologies, state-of-the-art techniques and their applications. It covers a variety of essential information fusion methods based on different techniques, including sparse/collaborative representation, kernel strategy,Bayesian models, metric learning, weight/classifier methods, and deep learning. The typical applications of these proposed fusion approaches are also presented, including image classification, domain adaptation, disease detection, image restoration, etc.
This book will benefit all researchers, professionals and graduate students in the fields of computer vision, pattern recognition, biometrics applications, etc. Furthermore, it offers a valuable resource for interdisciplinary research.
Inhaltsverzeichnis zu „Information Fusion “
Chapter 1. Introduction.- Chapter 2. Information fusion based on sparse/collaborative representation.- Chapter 3. Information fusion based on gaussian process latent variable model.- Chapter 4. Information fusion based on multi-view and multifeature earning.- Chapter 5. Information fusion based on metric learning.- Chapter 6. Information fusion based on score/weight classifier fusion.- Chapter 7. Information fusion based on deep learning.- Chapter 8. Conclusion.
Autoren-Porträt von Jinxing Li, Bob Zhang, David Zhang
Jinxing Li received his BSc degree from the Department of Automation, Hangzhou Dianzi University, Hangzhou, China, in 2012, his MSc degree from the Department of Automation, Chongqing University, China, in 2015, and his PhD from the Department of Computing, Hong Kong Polytechnic University, in 2018. His research interests include pattern recognition, medical biometrics and machine learning. Bob Zhang received the PhD degree in electrical and computer engineering from the University of Waterloo in 2011. After graduating, he was a postdoctoral researcher in the Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
He is currently an Associate Professor in the Department of Computer and Information Science, University of Macau. His research interests focus on biometrics, pattern recognition, and image processing. Dr. Zhang is a Senior Member of IEEE, a Technical Committee Member of the IEEE Systems, Man, and Cybernetics Society and an Associate Editor of Artificial Intelligence Review.
Bibliographische Angaben
- Autoren: Jinxing Li , Bob Zhang , David Zhang
- 2022, 1st ed. 2022, XXVI, 260 Seiten, Masse: 15,5 x 23,5 cm, Gebunden, Englisch
- Verlag: Springer, Berlin
- ISBN-10: 981168975X
- ISBN-13: 9789811689758
Sprache:
Englisch
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