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Table 1 Comparisons of recognition accuracies on KTH dataset

From: Recognizing flu-like symptoms from videos

Method

Brief description

Acc. %

ICPR’04 [22]

HOGHOF + SVM

71.7

CVPR’07 [36]

cuboid + WX-SVM

91.6

BMVC’09 [35]

cuboid + BoW+ χ2

89.1

 

HOGHOF + BoW + χ2

91.8

ECCV’10 [37]

convolutional nets

90.0

In this paper

cuboid + BoW + χ2 (baseline)

88.2

 

cuboid + AMK I U c S=2

91.7

 

cuboid + AMK I U b S=8

93.0

 

cuboid + AMK I B S=7

92.7

 

cuboid + AMK II

93.2

 

HOGHOF + BoW + χ2 (baseline)

88.5

 

HOGHOF + AMK I U c S=3

91.1

 

HOGHOF + AMK I U b S=27

95.2

 

HOGHOF + AMK I B S=5

92.3

 

HOGHOF + AMK II

93.5

  1. Comparisons of recognition accuracies on KTH dataset. Here shorthands of AMK I and II are used for AMK type I and type II kernels, respectively. U c (U b ) refers to the concentric (block) partition scheme in unary extension. B is for binary extension.