Visual saliency computation : a machine learning perspective
著者
書誌事項
Visual saliency computation : a machine learning perspective
Springer, c2014 , Produced by Amazon [manufacture]
- : pbk
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注記
Reprint. Originally published: Cham : Springer , c2014
Printed in Japan
Original issued in series: Lecture notes in computer science 8408
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
Benchmark and evaluation metrics.- Location-based visual saliency computation.- Object-based visual saliency computation.- Learning-based visual saliency computation.- Mining cluster-specific knowledge for saliency ranking.- Removing label ambiguity in training saliency model.- Saliency-based applications.- Conclusions and future work.
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