Fusion in Computer Vision

Understanding Complex Visual Content

  • Bogdan Ionescu
  • Jenny Benois-Pineau
  • Tomas Piatrik
  • Georges Quénot

Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Ningning Liu, Emmanuel Dellandréa, Bruno Tellez, Liming Chen
    Pages 1-28
  3. Marc T. Law, Nicolas Thome, Matthieu Cord
    Pages 29-52
  4. Sabin Tiberius Strat, Alexandre Benoit, Patrick Lambert, Hervé Bredin, Georges Quénot
    Pages 53-77
  5. Iván González-Díaz, Jenny Benois-Pineau, Vincent Buso, Hugo Boujut
    Pages 79-107
  6. Gregory K. Myers, Cees G. M. Snoek, Ramakant Nevatia, Ramesh Nallapati, Julien van Hout, Stephanie Pancoast et al.
    Pages 109-133
  7. Junshi Xia, Jocelyn Chanussot, Peijun Du, Xiyan He
    Pages 135-160
  8. Virginia Fernandez Arguedas, Qianni Zhang, Ebroul Izquierdo
    Pages 161-184
  9. Claire-Hélène Demarty, Cédric Penet, Bogdan Ionescu, Guillaume Gravier, Mohammad Soleymani
    Pages 185-208
  10. Alba García Seco de Herrera, Henning Müller
    Pages 209-228
  11. Martha Larson, Mark Melenhorst, María Menéndez, Peng Xu
    Pages 229-269
  12. Back Matter
    Pages 271-272

About this book

Introduction

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.

Keywords

Bagging and Boosting Methods Contextual Fusion Data Dimensionality Reduction Early Fusion Feature Fusion Hierarchical and Community-Based Fusion Kernel Fusion Late Fusion Legal, Ethical and Social Concepts of Fusion Metric Spaces Multimodal Fusion Normalization for Fusion Social Media Fusion and Mining

Editors and affiliations

  • Bogdan Ionescu
    • 1
  • Jenny Benois-Pineau
    • 2
  • Tomas Piatrik
    • 3
  • Georges Quénot
    • 4
  1. 1.University Politehnica of BucharestRomania
  2. 2.University of BordeauxTalenceFrance
  3. 3.Queen Mary University of LondonLondonUnited Kingdom
  4. 4.Lab. of Informatics of GrenobleFrance

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-05696-8
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-05695-1
  • Online ISBN 978-3-319-05696-8
  • Series Print ISSN 2191-6586
  • Series Online ISSN 2191-6594
  • About this book