Image classification from small sample, with distance learning and feature selection

Daphna Weinshall*, Lior Zamir

*Corresponding author for this work

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

1 Scopus citations


Small sample is an acute problem in many application domains, which may be partially addressed by feature selection or dimensionality reduction. For the purpose of distance learning, we describe a method for feature selection using equivalence constraints between pairs of datapoints. The method is based on L1 regularization and optimization. Feature selection is then incorporated into an existing non-parametric method for distance learning, which is based on the boosting of constrained generative models. Thus the final algorithm employs dynamical feature selection, where features are selected anew in each boosting iteration based on the weighted training data. We tested our algorithm on the classification of facial images, using two public domain databases. We show the results of extensive experiments where our method performed much better than a number of competing methods, including the original boosting-based distance learning method and two commonly used Mahalanobis metrics.

Original languageAmerican English
Title of host publicationAdvances in Visual Computing - Third International Symposium, ISVC 2007, Proceedings
PublisherSpringer Verlag
Number of pages10
EditionPART 2
ISBN (Print)9783540768555
StatePublished - 2007
Event3rd International Symposium on Visual Computing, ISVC 2007 - Lake Tahoe, NV, United States
Duration: 26 Nov 200728 Nov 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4842 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Symposium on Visual Computing, ISVC 2007
Country/TerritoryUnited States
CityLake Tahoe, NV


  • Distance learning
  • Feature selection
  • L1 regularization
  • Small sample


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