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Fusion in Computer Vision

  • Livre Relié
  • 272 Nombre de pages
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Visual content understanding is a complex and important challenge for applications in automatic multimedia information indexing, medicine, robotics, and surveillance. Yet the performance of such systems can be improved by the fusion of individual modalities/techniques for content representation and machine learning.

This comprehensive text/reference presents a thorough overview of Fusion in Computer Vision, from an interdisciplinary and multi-application viewpoint. Presenting contributions from an international selection of experts, the work describes numerous successful approaches, evaluated in the context of international benchmarks that model realistic use cases at significant scales.

Topics and features: examines late fusion approaches for concept recognition in images and videos, including the bag-of-words model; 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.

This authoritative collection is essential reading for researchers and students interested in the domain of information fusion for complex visual content understanding, and related fields.

Examines information fusion in the context of multimodal and multidimensional data representation, i.e., video, image and text

Presents a focus on information fusion for tackling higher-level description of multimedia information

Discusses the latest research on a broad range of multimedia information fusion techniques

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.

A Selective Weighted Late Fusion for Visual Concept RecognitionNingning Liu, Emmanuel Dellandréa, Bruno Tellez, and Liming ChenBag-of-Words Image Representation: Key Ideas and Further InsightMarc T. Law, Nicolas Thome, and Matthieu CordHierarchical Late Fusion for Concept Detection in VideosSabin Tiberius Strat, Alexandre Benoit, Patrick Lambert, Hervé Bredin, and Georges QuénotFusion of Multiple Visual Cues for Object Recognition in VideoI. Gonsalez-Diaz, J. Benois-Pineau, V. Buso, and H. BoujutEvaluating Multimedia Features and Fusion for Example-Based Event DetectionGregory K. Myers, Cees G.M. Snoek, Ramakant Nevatia, Ramesh Nallapati, Julien van Hout, Stephanie Pancoast, Chen Sun, Amirhossein Habibian, Dennis C. Koelma, Koen E. A. van de Sande, and Arnold W.M. SmeuldersRotation-Based Ensemble Classifiers for High Dimensional DataJunshi Xia, Jocelyn Chanussot, Peijun Du, and Xiyan HeMultimodal Fusion in Surveillance ApplicationsVirginia Fernandez Arguedas, Qianni Zhang, and Ebroul IzquierdoMultimodal Violence Detection in Hollywood Movies: State-of-the-Art and BenchmarkingClaire-Hélene Demarty and Cédric Penet and Bogdan Ionescu and Guillaume Gravier, and Mohammad SoleymaniFusion Techniques in Biomedical Information RetrievalAlba Garca Seco de Herrera and Henning MullerUsing Crowdsourcing to Capture Complexity in Human Interpretations of Multimedia ContentMartha Larson, Mark Melenhorst, Maria Menendez, and Peng Xu

Informations sur le produit

Titre: Fusion in Computer Vision
Code EAN: 9783319056951
ISBN: 978-3-319-05695-1
Format: Livre Relié
Editeur: Springer, Berlin
Genre: Informatique
nombre de pages: 272
Poids: g
Taille: H17mm x B243mm x T161mm
Année: 2014
Auflage: 2014

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