Fusion in Computer Vision: Understanding Complex Visual Content

Fusion in Computer Vision: Understanding Complex Visual Content

Hardcover(2014)

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Overview

Fusion in Computer Vision: Understanding Complex Visual Content by Bogdan Ionescu

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.

Product Details

ISBN-13: 9783319056951
Publisher: Springer International Publishing
Publication date: 03/26/2014
Series: Advances in Computer Vision and Pattern Recognition
Edition description: 2014
Pages: 272
Product dimensions: 6.10(w) x 9.25(h) x 0.03(d)

About the Author

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.

Table of Contents

A Selective Weighted Late Fusion for Visual Concept Recognition
Ningning Liu, Emmanuel Dellandréa, Bruno Tellez, and Liming Chen

Bag-of-Words Image Representation: Key Ideas and Further Insight
Marc T. Law, Nicolas Thome, and Matthieu Cord

Hierarchical Late Fusion for Concept Detection in Videos
Sabin Tiberius Strat, Alexandre Benoit, Patrick Lambert, Hervé Bredin, and Georges Quénot

Fusion of Multiple Visual Cues for Object Recognition in Video
I. Gonsalez-Diaz, J. Benois-Pineau, V. Buso, and H. Boujut

Evaluating Multimedia Features and Fusion for Example-Based Event Detection
Gregory 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. Smeulders

Rotation-Based Ensemble Classifiers for High Dimensional Data
Junshi Xia, Jocelyn Chanussot, Peijun Du, and Xiyan He

Multimodal Fusion in Surveillance Applications
Virginia Fernandez Arguedas, Qianni Zhang, and Ebroul Izquierdo

Multimodal Violence Detection in Hollywood Movies: State-of-the-Art and Benchmarking
Claire-Hélene Demarty and Cédric Penet and Bogdan Ionescu and Guillaume Gravier, and Mohammad Soleymani

Fusion Techniques in Biomedical Information Retrieval
Alba Garcıa Seco de Herrera and Henning Muller

Using Crowdsourcing to Capture Complexity in Human Interpretations of Multimedia Content
Martha Larson, Mark Melenhorst, Maria Menendez, and Peng Xu

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