Advances in Computer Vision and Pattern Recognition

Domain Adaptation in Computer Vision Applications

Editors: Csurka, Gabriela (Ed.)

  • The first book focused on domain adaptation for visual applications
  • Provides a comprehensive experimental study, highlighting the strengths and weaknesses of popular methods, and introducing new and more challenging datasets
  • Presents an historical overview of research in this area
  • Covers such tasks as object detection, image segmentation and video application, where the need for domain adaptation has been rarely addressed by the community
  • Considers real-world, industrial applications, and solutions for cases where existing methods might not be applicable
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eBook 91,62 €
price for Spain (gross)
  • ISBN 978-3-319-58347-1
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 114,39 €
price for Spain (gross)
  • ISBN 978-3-319-58346-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

This comprehensive text/reference presents a broad review of diverse domain adaptation (DA) methods for machine learning, with a focus on solutions for visual applications. The book collects together solutions and perspectives proposed by an international selection of pre-eminent experts in the field, addressing not only classical image categorization, but also other computer vision tasks such as detection, segmentation and visual attributes.

Topics and features: surveys the complete field of visual DA, including shallow methods designed for homogeneous and heterogeneous data as well as deep architectures; presents a positioning of the dataset bias in the CNN-based feature arena; proposes detailed analyses of popular shallow methods that addresses landmark data selection, kernel embedding, feature alignment, joint feature transformation and classifier adaptation, or the case of limited access to the source data; discusses more recent deep DA methods, including discrepancy-based adaptation networks and adversarial discriminative DA models; addresses domain adaptation problems beyond image categorization, such as a Fisher encoding adaptation for vehicle re-identification, semantic segmentation and detection trained on synthetic images, and domain generalization for semantic part detection; describes a multi-source domain generalization technique for visual attributes and a unifying framework for multi-domain and multi-task learning.

This authoritative volume will be of great interest to a broad audience ranging from researchers and practitioners, to students involved in computer vision, pattern recognition and machine learning.

About the authors

Dr. Gabriela Csurka is a Senior Scientist in the Computer Vision Team at Naver Labs Europe, Meylan, France.

Table of contents (16 chapters)

  • A Comprehensive Survey on Domain Adaptation for Visual Applications

    Csurka, Gabriela

    Pages 1-35

    Preview Buy Chapter 30,19 €
  • A Deeper Look at Dataset Bias

    Tommasi, Tatiana (et al.)

    Pages 37-55

    Preview Buy Chapter 30,19 €
  • Geodesic Flow Kernel and Landmarks: Kernel Methods for Unsupervised Domain Adaptation

    Gong, Boqing (et al.)

    Pages 59-79

    Preview Buy Chapter 30,19 €
  • Unsupervised Domain Adaptation Based on Subspace Alignment

    Fernando, Basura (et al.)

    Pages 81-94

    Preview Buy Chapter 30,19 €
  • Learning Domain Invariant Embeddings by Matching Distributions

    Baktashmotlagh, Mahsa (et al.)

    Pages 95-114

    Preview Buy Chapter 30,19 €

Buy this book

eBook 91,62 €
price for Spain (gross)
  • ISBN 978-3-319-58347-1
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 114,39 €
price for Spain (gross)
  • ISBN 978-3-319-58346-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Domain Adaptation in Computer Vision Applications
Editors
  • Gabriela Csurka
Series Title
Advances in Computer Vision and Pattern Recognition
Copyright
2017
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG
eBook ISBN
978-3-319-58347-1
DOI
10.1007/978-3-319-58347-1
Hardcover ISBN
978-3-319-58346-4
Series ISSN
2191-6586
Edition Number
1
Number of Pages
X, 344
Number of Illustrations and Tables
6 b/w illustrations, 101 illustrations in colour
Topics