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The One-Shot Similarity Kernel
Lior Wolf1
Tal Hassner2
Yaniv Taigman1,3
1. The School of Computer Science,
Tel-Aviv University, Israel
2. Computer Science Division
The Open University of Israel
3. face.com,
Tel-Aviv, Israel
Abstract: The One-Shot similarity measure has recently been introduced in the
context of face recognition where it was used to produce state-of-the-art results.
Given two vectors, their One-Shot similarity score reflects the likelihood of each
vector belonging in the same class as the other vector and not in a class defined
by a fixed set of "negative" examples. The potential of this approach has thus far
been largely unexplored. In this paper we analyze the One-Shot score and show that:
(1) when using a version of LDA as the underlying classifier, this score is a Conditionally
Positive Definite kernel and may be used within kernel-methods (e.g., SVM), (2)
it can be efficiently computed, and (3) that it is effective as an underlying mechanism
for image representation. We further demonstrate the effectiveness of the One-Shot
similarity score in a number of applications including multi-class identification
anddescriptor generation.
Reference:
Lior Wolf, Tal Hassner, and Yaniv Taigman,
"The One-Shot Similarity Kernel," IEEE International Conference on Computer
Vision (ICCV), Sept. 2009.
Click here for the PDF
(Note - Extended version!)
Click here for the BibTex
Related publications:
Multiple One-Shots for Utilizing Class Label Information
Descriptor Based Methods in the Wild
MATLAB Code for the LDA based One-Shot Similarity Measure
Below please find MATLAB code for computing the One-Shot Similarity score using
LDA as an underlying classifier. Please report any bugs or problems to
talhassner@gmail.com.
Type "help oss_lda_sA_from_xsn" or "help oss_lda_score" for more information on
each of these functions.
A typical usage would look something like this:
>> sA = oss_lda_sA_from_xsn(XSN);
>> Score1 = oss_lda_score(x1,x2,sA);
>> Score2 = oss_lda_score(x2,x1,sA);
>> Score = (Score1 + Score2)./2;
Score is then the symmetric One-Shot Similarity of the two vectors x1 and x2.
Downloads:
oss_lda_sA_from_xsn.m
oss_lda_score.m
Copyright and disclaimer:
Copyright 2009, Lior Wolf, Tal Hassner, and Yaniv Taigman
Copyright: no material is allowed to be
copied or used in any way without written permission of the authors.
Last update 22th of July, 2009
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