Fusion in Computer Vision
Understanding Complex Visual Content
(Sprache: Englisch)
This book presents a thorough overview of fusion in computer vision, from an interdisciplinary and multi-application viewpoint, describing successful approaches, evaluated in the context of international benchmarks that model realistic use cases. Features:...
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Klappentext zu „Fusion in Computer Vision “
This book presents a thorough overview of fusion in computer vision, from an interdisciplinary and multi-application viewpoint, describing successful approaches, evaluated in the context of international benchmarks that model realistic use cases. Features: examines late fusion approaches for concept recognition in images and videos; describes the interpretation of visual content by incorporating models of the human visual system with content understanding methods; investigates the fusion of multi-modal features of different semantic levels, as well as results of semantic concept detections, for example-based event recognition in video; proposes rotation-based ensemble classifiers for high-dimensional data, which encourage both individual accuracy and diversity within the ensemble; reviews application-focused strategies of fusion in video surveillance, biomedical information retrieval, and content detection in movies; discusses the modeling of mechanisms of human interpretation of complex visual content.
Inhaltsverzeichnis zu „Fusion in Computer Vision “
A Selective Weighted Late Fusion for Visual Concept Recognition.- Bag-of-Words Image Representation: Key Ideas and Further Insight.- Hierarchical Late Fusion for Concept Detection in Videos.- Fusion of Multiple Visual Cues for Object Recognition in Video.- Evaluating Multimedia Features and Fusion for Example-Based Event Detection.- Rotation-Based Ensemble Classifiers for High Dimensional Data.- Multimodal Fusion in Surveillance Applications.- Multimodal Violence Detection in Hollywood Movies: State-of-the-Art and Benchmarking.- Fusion Techniques in Biomedical Information Retrieval.- Using Crowdsourcing to Capture Complexity in Human Interpretations of Multimedia Content.
Autoren-Porträt
Dr. Bogdan Ionescu is a lecturer and Coordinator of the Video Processing Group at the Image Processing and Analysis Laboratory, University Politehnica of Bucharest, Romania. Dr. Jenny Benois-Pineau is a full professor and Chair of the Video Analysis and Indexing research group at the University of Bordeaux, France. Dr. Tomas Piatrik is a senior researcher in the Multimedia and Vision Research Group at Queen Mary University of London, UK. Dr. Georges Quénot is a senior researcher at CNRS and leader of the Multimedia Information Modeling and Retrieval group at the Grenoble Informatics Laboratory, France.
Bibliographische Angaben
- 2016, Softcover reprint of the original 1st ed. 2014, XIV, 272 Seiten, 65 farbige Abbildungen, Masse: 17 x 23,4 cm, Kartoniert (TB), Englisch
- Herausgegeben: Bogdan Ionescu, Jenny Benois-Pineau, Tomas Piatrik, Georges Quénot
- Verlag: Springer, Berlin
- ISBN-10: 3319347748
- ISBN-13: 9783319347745
Sprache:
Englisch
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