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 multiclass
identification and descriptor generation.