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Titlebook: Dictionary Learning in Visual Computing; Qiang Zhang,Baoxin Li Book 2015 Springer Nature Switzerland AG 2015

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發(fā)表于 2025-3-21 16:56:26 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Dictionary Learning in Visual Computing
編輯Qiang Zhang,Baoxin Li
視頻videohttp://file.papertrans.cn/272/271026/271026.mp4
叢書名稱Synthesis Lectures on Image, Video, and Multimedia Processing
圖書封面Titlebook: Dictionary Learning in Visual Computing;  Qiang Zhang,Baoxin Li Book 2015 Springer Nature Switzerland AG 2015
描述The last few years have witnessed fast development on dictionary learning approaches for a set of visual computing tasks, largely due to their utilization in developing new techniques based on sparse representation. Compared with conventional techniques employing manually defined dictionaries, such as Fourier Transform and Wavelet Transform, dictionary learning aims at obtaining a dictionary adaptively from the data so as to support optimal sparse representation of the data. In contrast to conventional clustering algorithms like K-means, where a data point is associated with only one cluster center, in a dictionary-based representation, a data point can be associated with a small set of dictionary atoms. Thus, dictionary learning provides a more flexible representation of data and may have the potential to capture more relevant features from the original feature space of the data. One of the early algorithms for dictionary learning is K-SVD. In recent years, many variations/extensionsof K-SVD and other new algorithms have been proposed, with some aiming at adding discriminative capability to the dictionary, and some attempting to model the relationship of multiple dictionaries. One
出版日期Book 2015
版次1
doihttps://doi.org/10.1007/978-3-031-02253-1
isbn_softcover978-3-031-01125-2
isbn_ebook978-3-031-02253-1Series ISSN 1559-8136 Series E-ISSN 1559-8144
issn_series 1559-8136
copyrightSpringer Nature Switzerland AG 2015
The information of publication is updating

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發(fā)表于 2025-3-21 20:46:46 | 只看該作者
An Instructive Case Study with Face Recognition,n, finding a solution (or an approximate solution) for the learning task under the formulation, understanding the behavior of the the solution (e.g., convergence analysis of an optimization algorithm), developing inference schemes such as classification (if needed) under the learned dictionary, and
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發(fā)表于 2025-3-22 06:53:27 | 只看該作者
1559-8136 en proposed, with some aiming at adding discriminative capability to the dictionary, and some attempting to model the relationship of multiple dictionaries. One978-3-031-01125-2978-3-031-02253-1Series ISSN 1559-8136 Series E-ISSN 1559-8144
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https://doi.org/10.1007/978-94-010-3601-6n, finding a solution (or an approximate solution) for the learning task under the formulation, understanding the behavior of the the solution (e.g., convergence analysis of an optimization algorithm), developing inference schemes such as classification (if needed) under the learned dictionary, and
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https://doi.org/10.1007/978-94-010-3601-6t literature suggests that significant progresses have been obtained by recent approaches based on the general dictionary learning idea, when compared with more conventional approaches. As the focus of this book is on visual computing applications, we now illustrate how the general idea has been ada
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