Reverse Training: An efficient approach for image set classification

Munawar Hayat, Mohammed Bennamoun, Senjian An

Research output: A Conference proceeding or a Chapter in BookConference contribution

15 Citations (Scopus)
1 Downloads (Pure)

Abstract

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
EditorsDavid Fleet, Tomas Pajdla, Bernt Schiele, Tinne Tuytelaars
Place of PublicationSwitzerland
PublisherSpringer
Pages784-799
Number of pages16
Volume8694
ISBN (Electronic)9783319105994
ISBN (Print)9783319105987
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event13th European Conference on Computer Vision - Zurich, Zurich, Switzerland
Duration: 6 Sep 201410 Sep 2014

Conference

Conference13th European Conference on Computer Vision
CountrySwitzerland
CityZurich
Period6/09/1410/09/14

Fingerprint

Classifiers
Object recognition
Face recognition
Experiments

Cite this

Hayat, M., Bennamoun, M., & An, S. (2014). Reverse Training: An efficient approach for image set classification. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.), Computer vision-ECCV 2014 (Vol. 8694, pp. 784-799). Switzerland: Springer. https://doi.org/10.1007/978-3-319-10599-4_50
Hayat, Munawar ; Bennamoun, Mohammed ; An, Senjian. / Reverse Training: An efficient approach for image set classification. Computer vision-ECCV 2014. editor / David Fleet ; Tomas Pajdla ; Bernt Schiele ; Tinne Tuytelaars. Vol. 8694 Switzerland : Springer, 2014. pp. 784-799
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Hayat, M, Bennamoun, M & An, S 2014, Reverse Training: An efficient approach for image set classification. in D Fleet, T Pajdla, B Schiele & T Tuytelaars (eds), Computer vision-ECCV 2014. vol. 8694, Springer, Switzerland, pp. 784-799, 13th European Conference on Computer Vision, Zurich, Switzerland, 6/09/14. https://doi.org/10.1007/978-3-319-10599-4_50

Reverse Training: An efficient approach for image set classification. / Hayat, Munawar; Bennamoun, Mohammed; An, Senjian.

Computer vision-ECCV 2014. ed. / David Fleet; Tomas Pajdla; Bernt Schiele; Tinne Tuytelaars. Vol. 8694 Switzerland : Springer, 2014. p. 784-799.

Research output: A Conference proceeding or a Chapter in BookConference contribution

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Hayat M, Bennamoun M, An S. Reverse Training: An efficient approach for image set classification. In Fleet D, Pajdla T, Schiele B, Tuytelaars T, editors, Computer vision-ECCV 2014. Vol. 8694. Switzerland: Springer. 2014. p. 784-799 https://doi.org/10.1007/978-3-319-10599-4_50