Visual Saliency Computation : A Machine Learning Perspective

Wen Gao

ISBN 10: 3319056417 / ISBN 13: 9783319056418

Published by Springer International Publishing, 2014

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783319056418

Bibliographic Details

• Title: Visual Saliency Computation : A Machine Learning Perspective
• Publisher: Springer International Publishing
• Publication Date: 2014
• Language: English
• ISBN 10: 3319056417
• ISBN 13: 9783319056418
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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.