Covariances in Computer Vision and Machine Learning (Synthesis Lectures on Computer Vision) - Image 1

£43.30

Springer Covariances in Computer Vision and Machine Learning (Synthesis Lectures on Computer Vision)

Name: Covariances in Computer Vision and Machine Learning (Synthesis Lectures on Computer Vision)
Brand: Springer
Price: 43.3 GBP
Availability: InStock

AI & Machine Learning

This is the most expensive it has ever been. Walk away.

£43 today · previous high £43 · all-time low £42

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 219 days • 219 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 219 days • 2 price levels

Days at Price

Current Price

Price Analysis

Most common price: £43 (195 days, 89.0%)

Price range: £42 - £43

Price levels: 2 different prices over 219 days

Description

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} representation via 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. Computationally, we focus on kernel methods on covariance operators, especially using the Log-Hilbert-Schmidt distance. Specifically, we present a two-layer kernel machine, using the Log-Hilbert-Schmidt distance and its finite-dimensional approximation, which reduces the computational complexity of the exact formulation while largely preserving its capability. Theoretical analysis shows that, mathematically, the approximate Log-Hilbert-Schmidt distance should be preferred over the approximate Log-Hilbert-Schmidt inner product and, computationally, it should be preferred over the approximate affine-invariant Riemannian distance. Numerical experiments on image classification demonstrate significant improvements of the infinite-dimensional formulation over the finite-dimensional counterpart. Given the numerous applications of covariance matrices in many areas of mathematics, statistics, and machine learning, just to name a few, we expect that the infinite-dimensional covariance operator formulation presented here will have many more applications beyond those in computer vision.

Product Specifications

Brand: Springer
Format: paperback
ASIN: 3031006925
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 07 November 2017
Listed Since: 01 June 2022

Barcode

No barcode data available

Springer Covariances in Computer Vision and Machine Learning (Synthesis Lectures on Computer Vision)

This is the most expensive it has ever been. Walk away.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

High-Dimensional Covariance Matrix Estimation: An Introduction to Random Matrix Theory (SpringerBriefs in Applied Statistics and Econometrics)

Matrix Analysis and Entrywise Positivity Preservers: 471 (London Mathematical Society Lecture Note Series, Series Number 471)

Large Covariance and Autocovariance Matrices (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

The Analysis of Covariance and Alternatives: Statistical Methods for Experiments, Quasi-Experiments, and Single-Case Studies (Wiley Series in Probability and Statistics)

Large Covariance and Autocovariance Matrices (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

Dense Image Correspondences for Computer Vision

Kernel Mode Decomposition and the Programming of Kernels: 8 (Surveys and Tutorials in the Applied Mathematical Sciences, 8)

Learning OpenCV 3 Application Development

Digital Image Processing and Analysis: Computer Vision and Image Analysis

Random Circulant Matrices

Foundations of Computer Vision: Computational Geometry, Visual Image Structures and Object Shape Detection: 124 (Intelligent Systems Reference Library, 124)

Generalized Principal Component Analysis: 40 (Interdisciplinary Applied Mathematics, 40)

Kernel Learning Algorithms for Face Recognition

Foundations of Computer Vision: Computational Geometry, Visual Image Structures and Object Shape Detection: 124 (Intelligent Systems Reference Library, 124)

Advances in Low-Level Color Image Processing: 11 (Lecture Notes in Computational Vision and Biomechanics, 11)

Practical Linear Algebra: A Geometry Toolbox (Textbooks in Mathematics)

BRIDGING THE GAP BETWEEN GRAPH EDIT DISTANCE AND KERNEL MACHINES: 68 (Series In Machine Perception And Artificial Intelligence)

Computer Vision – ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012. Proceedings, Part V: 7576 (Lecture Notes in Computer Science, 7576)

Advances in Computer Vision: Proceedings of the 2019 Computer Vision Conference (CVC), Volume 1: 943 (Advances in Intelligent Systems and Computing, 943)

Computer Vision – ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part II: 7573 (Lecture Notes in Computer Science, 7573)

Computer Vision – ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012. Proceedings, Part IV: 7575 (Lecture Notes in Computer Science, 7575)

Computer Vision - ACCV 2010: 10th Asian Conference on Computer Vision, Queenstown, New Zealand, November 8-12, 2010, Revised Selected Papers, Part III: 6494 (Lecture Notes in Computer Science, 6494)

Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing

Basics of Matrix Algebra for Statistics with R (Chapman & Hall/CRC The R Series)