| 書目名稱 | Covariances in Computer Vision and Machine Learning | | 編輯 | Hà Quang Minh,Vittorio Murino | | 視頻video | http://file.papertrans.cn/240/239189/239189.mp4 | | 叢書名稱 | Synthesis Lectures on Computer Vision | | 圖書封面 |  | | 描述 | .Covariance matrices play important roles in many areas of mathematics, statistics, and machine learning, as well as their applications. In computer vision and image processing, they give rise to a powerful data representation, namely the covariance descriptor, with numerous practical applications...In this book, we begin by presenting an overview of the {it finite-dimensional covariance matrix} representation approach of images, along with its statistical interpretation. In particular, we discuss the various distances and divergences that arise from the intrinsic geometrical structures of the set of Symmetric Positive Definite (SPD) matrices, namely Riemannian manifold and convex cone structures. Computationally, we focus on kernel methods on covariance matrices, especially using the Log-Euclidean distance...We then show some of the latest developments in the generalization of the finite-dimensional covariance matrix representation to the {it infinite-dimensional covariance operator} representationvia positive definite kernels. We present the generalization of the affine-invariant Riemannian metric and the Log-Hilbert-Schmidt metric, which generalizes the Log-Euclidean distance. C | | 出版日期 | Book 2018 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-01820-6 | | isbn_softcover | 978-3-031-00692-0 | | isbn_ebook | 978-3-031-01820-6Series ISSN 2153-1056 Series E-ISSN 2153-1064 | | issn_series | 2153-1056 | | copyright | Springer Nature Switzerland AG 2018 |
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