One shot similarity metric learning for action recognition

Orit Kliper-Gross, Tal Hassner, Lior Wolf

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The One-Shot-Similarity (OSS) is a framework for classifier-based similarity functions. It is based on the use of background samples and was shown to excel in tasks ranging from face recognition to document analysis. However, we found that its performance depends on the ability to effectively learn the underlying classifiers, which in turn depends on the underlying metric. In this work we present a metric learning technique that is geared toward improved OSS performance. We test the proposed technique using the recently presented ASLAN action similarity labeling benchmark. Enhanced, state of the art performance is obtained, and the method compares favorably to leading similarity learning techniques.

Original languageEnglish
Title of host publicationSimilarity-Based Pattern Recognition - First International Workshop, SIMBAD 2011, Proceedings
Number of pages15
StatePublished - 2011
Event1st International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2011 - Venice, Italy
Duration: 28 Sep 201130 Sep 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7005 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference1st International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2011


  • Action Similarity
  • Learned metrics
  • One-Shot-Similarity


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