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Record Nr. |
UNINA9910485048203321 |
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Titolo |
Visual Saliency Computation : A Machine Learning Perspective / / edited by Jia Li, Wen Gao |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
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ISBN |
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Edizione |
[1st ed. 2014.] |
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Descrizione fisica |
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1 online resource (XII, 240 p. 100 illus.) |
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Collana |
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Image Processing, Computer Vision, Pattern Recognition, and Graphics ; ; 8408 |
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Disciplina |
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Soggetti |
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Optical data processing |
Artificial intelligence |
Data mining |
Image Processing and Computer Vision |
Artificial Intelligence |
Data Mining and Knowledge Discovery |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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"Conference papers and proceedings." |
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Nota di contenuto |
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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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Sommario/riassunto |
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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, |
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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. |
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