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Record Nr. |
UNINA9910299826503321 |
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Autore |
Xu Long |
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Titolo |
Visual Quality Assessment by Machine Learning / / by Long Xu, Weisi Lin, C.-C. Jay Kuo |
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Pubbl/distr/stampa |
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Singapore : , : Springer Singapore : , : Imprint : Springer, , 2015 |
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ISBN |
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Edizione |
[1st ed. 2015.] |
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Descrizione fisica |
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1 online resource (142 p.) |
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Collana |
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SpringerBriefs in Signal Processing, , 2196-4076 |
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Disciplina |
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Soggetti |
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Signal processing |
Image processing |
Speech processing systems |
Optical data processing |
Computational intelligence |
Signal, Image and Speech Processing |
Image Processing and Computer Vision |
Computational Intelligence |
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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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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters. |
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Nota di contenuto |
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Introduction -- Fundamental knowledges of machine learning -- Image features and feature processing -- Feature pooling by learning -- Metrics fusion -- Summary and remarks for future research. |
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Sommario/riassunto |
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The book encompasses the state-of-the-art visual quality assessment (VQA) and learning based visual quality assessment (LB-VQA) by providing a comprehensive overview of the existing relevant methods. It delivers the readers the basic knowledge, systematic overview and new development of VQA. It also encompasses the preliminary knowledge of Machine Learning (ML) to VQA tasks and newly developed ML techniques for the purpose. Hence, firstly, it is particularly helpful to the beginner-readers (including research students) to enter into VQA field in general and LB-VQA one in particular. Secondly, new development in VQA and LB-VQA particularly are detailed in this book, which will give peer researchers and engineers new insights in VQA. |
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