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

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Preface
Chapter 1: Introduction

I Theory

Chapter 2: Nearest-Neighbor Searching and Metric Space Dimensions by K. L. Clarkson.
Chapter 3: _Locality-Sensitive Hashing Using Stable Distributions_by A. Andoni, M. Datar, N. Immorlica, P. Indyk, and V. Mirrokni.

II Learning

Chapter 4: New Algorithms for Efficient High-Dimensional Nonparametric Classification by T. Liu, A. W. Moore, and A. Gray.
Chapter 5: Approximate Nearest Neighbor Regression in Very High Dimensions by S. Vijayakumar, A. D'Souza, and S. Schaal.
Chapter 6: Learning Embeddings for Fast Approximate Nearest Neighbor Retrieval by V. Athitsos, J. Alon, S. Sclaroff, andG. Kollios.

III Vision

Chapter 7: _Parameter-Sensitive Hashing for Fast Pose Estimation_by G. Shakhnarovich,P. Viola, and T. Darrell.
Chapter 8: _Contour Matching Using Approximate Earth Mover's Distance_by K. Grauman and T. Darrell.
Chapter 9: Adaptive Mean Shift Based Clustering in High Dimensions by I. Shimshoni, , and P. Meer.
Chapter 10: Object Recognition using Locality Sensitive Hashing of Shape Contexts by A. Frome and J. Malik.