NEAREST-NEIGHBOR METHODS IN LEARNING AND VISION: THEORY AND PRACTICE

Le Monde En Tique

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Ouvrage 9780262195478 : NEAREST-NEIGHBOR METHODS IN LEARNING AND VISION: THEORY AND PRACTICE
This volume presents theoretical and practical discussions of nearest-neighbor (NN) methods in machine learning and examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic. It brings together contributions from researchers in theory of computation, machine learning, and computer vision with the goals of bridging the gaps between disciplines and presenting state-of-the-art methods for emerging applications.
The contributors focus on the importance of designing algorithms for NN search, and for the related classification, regression, and retrieval tasks, that remain efficient even as the number of points or the dimensionality of the data grows very large. The book begins with two theoretical chapters on computational geometry and then explores ways to make the NN approach practicable in machine learning applications where the dimensionality of the data and the size of the data sets make the naïve methods for NN search prohibitively expensive. The final chapters describe successful applications of an NN algorithm, locality-sensitive hashing (LSH), to vision tasks.
Gregory Shakhnarovich is a Postdoctoral Research Associate in the Computer Science Department at Brown University
Trevor Darrell is Associate Professor and Head of the Vision Interface Group in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT.
Piotr Indyk is Associate Professor in the Theory of Computation Group in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT.
Table of contents :
Series Foreword
Preface
Introduction
Gregory Shakhnarovich, Piotr Indyk and Trevor Darrell
ITheory13 2Nearest-Neighbor Searching and Metric Space Dimensions
Kenneth L. Clarkson15 3Locality-Sensitive Hashing Using Stable Distributions
Aleksandr Andoni, Mayur Datar, Nicole Immorlica, Piotr Indyk and Vahab Mirrokni61 IIApplications: Learning73 4New Algorithms for Efficient High-Dimensional Nonparametric Classification
Ting Liu, Andrew W. Moore and Alexander Gray75 5Approximate Nearest Neighbor Regression in Very High Dimensions
Sethu Vijayakumar, Aaron D'Souza and Stefan Schaal103 6Learning Embeddings for Fast Approximate Nearest Neighbor Retrieval
Vassilis Athitsos, Jonathan Alon, Stan Sclaroff and George Kollios143 IIIApplications: Vision163 7Parameter-Sensitive Hashing for Fast Pose Estimation
Gregory Shakhnarovich, Paul Viola and Trevor Darrell165 8Contour Matching Using Approximate Earth Mover's Distance
Kristen Grauman and Trevor Darrell181 9Adaptive Mean Shift Based Clustering in High Dimensions
Ilan Shimshoni, Bogdan Georgescu and Peter Meer203 10Object Recognition using Locality Sensitive Hashing of Shape Contexts
Andrea Frome and Jitendra Malik221 Contributors
Index


Auteur : DARRELL
Editeur : M.I.T. PRESS
Nombre de pages : 280
Date de publication : 03 2006

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