Visual Saliency Computation

A Machine Learning Perspective

  • Jia Li
  • Wen Gao

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8408)

Table of contents

  1. Front Matter
  2. Jia Li, Wen Gao
    Pages 1-21
  3. Jia Li, Wen Gao
    Pages 23-44
  4. Jia Li, Wen Gao
    Pages 45-71
  5. Jia Li, Wen Gao
    Pages 73-100
  6. Jia Li, Wen Gao
    Pages 101-149
  7. Jia Li, Wen Gao
    Pages 215-232
  8. Jia Li, Wen Gao
    Pages 233-237
  9. Back Matter

About this book


This book covers fundamental principles and computational approaches relevant to visual saliency computation. As an interdisciplinary problem, visual saliency computation is introduced in this book from an innovative perspective that combines both neurobiology and machine learning. The book is also well-structured to address a wide range of readers, from specialists in the field to general readers interested in computer science and cognitive psychology. With this book, a reader can start from the very basic question of "what is visual saliency?" and progressively explore the problems in detecting salient locations, extracting salient objects, learning prior knowledge, evaluating performance, and using saliency in real-world applications. It is highly expected that this book will spark a great interest of research in the related communities in years to come.


artificial intelligence benchmarks computer vision human cognition human vision image processing information processing machine learning neurobiology performance evaluation saliency detection salient objects salient region detections video processing visual attention visual saliency computation

Authors and affiliations

  • Jia Li
    • 1
  • Wen Gao
    • 1
  1. 1.Peking UniversityBeijingChina

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-05641-8
  • Online ISBN 978-3-319-05642-5
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site