Michael Werman Ofir Pele Brian Kulis

#### Abstract

Distance functions are at the core of numerous computer vision tasks. This tutorial provides an
in-depth introduction to existing distances that can be used in computer vision and methods for
learning new distances via metric learning. The tutorial has two parts. In the first part, we cover
several existing distances, discuss their properties, and show how and when to apply them. We give
special emphasis on Earth Mover's Distance variants and approximations, and efficient algorithms
for their computation. In the second part, we cover metric learning methods; these methods aim to
learn an appropriate distance/similarity function for a given task. We focus on learning
Mahalanobis distance functions (a.k.a. quadratic forms), and also cover non-linear and
non-Mahalanobis metric learning methods. Throughout this part, we will focus on a variety of
existing vision applications of metric learning. For this tutorial, we only assume that attendees
know some basic mathematics and computer science.

## Where & When |
||

ECCV 2010, Hersonissos, Crete | ||

Creta Maris Hotel, Minous south | ||

Sunday 5 September 2010, 14:00-18:00 | ||

## Material | ||

Part 1: Distance Functions | ||

ppt
pdf(color 6 in pg) pdf(b&w 6 in pg) QC code FastEMD code | ||

Part 2: Metric Learning | ||