Titlebook: Covariances in Computer Vision and Machine Learning; Hà Quang Minh,Vittorio Murino Book 2018 Springer Nature Switzerland AG 2018

陶醉

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

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

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

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, along

Defense

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 s

AXIS

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

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