Reverse training: an efficient approach for image set classification

Munawar Hayat, Mohammed Bennamoun, Senjian An

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15 Citations (Scopus)


This paper introduces a new approach, called reverse training, to efficiently extend binary classifiers for the task of multi-class image set classification. Unlike existing binary to multi-class extension strategies, which require multiple binary classifiers, the proposed approach is very efficient since it trains a single binary classifier to optimally discriminate the class of the query image set from all others. For this purpose, the classifier is trained with the images of the query set (labelled positive) and a randomly sampled subset of the training data (labelled negative). The trained classifier is then evaluated on rest of the training images. The class of these images with their largest percentage classified as positive is predicted as the class of the query image set. The confidence level of the prediction is also computed and integrated into the proposed approach to further enhance its robustness and accuracy. Extensive experiments and comparisons with existing methods show that the proposed approach achieves state of the art performance for face and object recognition on a number of datasets.

Original languageEnglish
Title of host publicationComputer Vision, ECCV 2014
Subtitle of host publication13th European Conference Zurich, Switzerland, September 6-12, 2014 Proceedings, Part VI
EditorsDavid Fleet, Tomas Pajdla, Bernt Schiele, Tinne Tuytelaars
Place of PublicationCham Switzerland
Number of pages16
ISBN (Electronic)9783319105994
ISBN (Print)9783319105987
Publication statusPublished - 2014
Externally publishedYes
EventEuropean Conference on Computer Vision 2014 - Zurich, Switzerland
Duration: 6 Sep 201412 Sep 2014
Conference number: 13th (Proceedings)

Publication series

NameLecture Notes in Computer Science
NumberPART 6
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceEuropean Conference on Computer Vision 2014
Abbreviated titleECCV 2014
Internet address


  • Face and Object Recognition
  • Image Set Classification

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