Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Hardcover)

Gregory Shakhnarovich, Trevor Darrell, Piotr Indyk

  • 出版商: MIT
  • 出版日期: 2006-03-24
  • 定價: $1,350
  • 售價: 3.0$399
  • 語言: 英文
  • 頁數: 280
  • 裝訂: Hardcover
  • ISBN: 026219547X
  • ISBN-13: 9780262195478

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Regression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, may alleviate the problems in using these methods on large data sets. 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
1 Introduction
Gregory Shakhnarovich, Piotr Indyk and Trevor Darrell
I Theory 13
2 Nearest-Neighbor Searching and Metric Space Dimensions
Kenneth L. Clarkson 15
3 Locality-Sensitive Hashing Using Stable Distributions
Aleksandr Andoni, Mayur Datar, Nicole Immorlica, Piotr Indyk and Vahab Mirrokni 61
II Applications: Learning 73
4 New Algorithms for Efficient High-Dimensional Nonparametric Classification
Ting Liu, Andrew W. Moore and Alexander Gray 75
5 Approximate Nearest Neighbor Regression in Very High Dimensions
Sethu Vijayakumar, Aaron D'Souza and Stefan Schaal 103
6 Learning Embeddings for Fast Approximate Nearest Neighbor Retrieval
Vassilis Athitsos, Jonathan Alon, Stan Sclaroff and George Kollios 143
III Applications: Vision 163
7 Parameter-Sensitive Hashing for Fast Pose Estimation
Gregory Shakhnarovich, Paul Viola and Trevor Darrell 165
8 Contour Matching Using Approximate Earth Mover's Distance
Kristen Grauman and Trevor Darrell 181
9 Adaptive Mean Shift Based Clustering in High Dimensions
Ilan Shimshoni, Bogdan Georgescu and Peter Meer 203
10 Object Recognition using Locality Sensitive Hashing of Shape Contexts
Andrea Frome and Jitendra Malik 221