陶醉 发表于 2025-3-26 21:14:09
Conclusion and Future Outlookmodel . in the input data, can substantially outperform finite-dimensional covariance matrices, which only model . in the input. This performance gain comes at higher computational costs and we showed how to substantially decrease these costs via approximation methods.Tidious 发表于 2025-3-27 01:58:25
measures between images can then be chosen to be distances/divergences between the corresponding covariance matrices, or equivalently, distances/divergences between the corresponding multivariate Gaussian probability distributions, which will be presented in Chapter 2.Cumbersome 发表于 2025-3-27 05:57:04
Data Representation by Covariance Matrices measures between images can then be chosen to be distances/divergences between the corresponding covariance matrices, or equivalently, distances/divergences between the corresponding multivariate Gaussian probability distributions, which will be presented in Chapter 2.Musculoskeletal 发表于 2025-3-27 10:13:43
2153-1056 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, alongDefense 发表于 2025-3-27 17:20:40
Book 2018ision 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 sAXIS 发表于 2025-3-27 19:21:29
Kernel Methods on Covariance Operatorsrnel machine with the Log-Euclidean distance and inner product presented in Chapter 3 can be viewed as a special case of this framework, with the kernel in the first layer being the linear kernel. Along with kernels defined using the exact Log-Hilbert-Schmidt distance, we present kernels defined usi文件夹 发表于 2025-3-27 22:26:17
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