Open Access

Erratum to: Refining deep convolutional features for improving fine-grained image recognition

  • Weixia Zhang1,
  • Jia Yan1,
  • Wenxuan Shi2,
  • Tianpeng Feng1 and
  • Dexiang Deng1Email author
EURASIP Journal on Image and Video Processing20172017:33

https://doi.org/10.1186/s13640-017-0183-4

Received: 3 May 2017

Accepted: 3 May 2017

Published: 8 May 2017

The original article was published in EURASIP Journal on Image and Video Processing 2017 2017:27

1 Erratum

Upon publication of the original article [1], it was noticed that there were several blanks in the Table 5 and the footnote of the Table 5, ‘The 'n/a' entries in the table means that bounding box or part annotation is not used.’ was incorrectly given as ‘The 'n/a' entries in the table means that the results are not available.’ This has now been acknowledged and corrected in this erratum. This has now been incorporated in the new Table 5 shown below.
Table 5

Comparison of performance of our methods with some recent state-of-the-arts methods in cub. BBox, Parts denote bounding-box and parts annotation respectively

Methods

Train phase

Test phase

Dim.

Model

Acc.

DPD

  

Dataset: cub

    

Part-Stacked CNN [1]

BBox + Parts

BBox

4,096

Part-Stacked CNN

76.2%

1.484

Deep LAC [2]

BBox + Parts

BBox

12,288

Alex-Net

80.3%

0.521

PN-CNN [3]

BBox + Parts

n/a

13,512

Alex-Net

85.4%

0.506

PG-Alignment [4]

BBox

n/a

126,976

VGG-19

82.8%

0.052

Symbolic [5]

BBox

BBox

20,992

Shallow feature: SIFT

59.4%

0.226

Cross layer pooling[6]

BBox

BBox

4,096

Alex-Net

73.5%

1.436

Mask-CNN [12]

Parts

n/a

8,192

VGG-16 + FCN

85.4%

0.834

Spatial Transformer CNN [33]

n/a

n/a

4,096

ST-CNN

84.1%

1.643

Bilinear CNN [8]

n/a

n/a

262,144

VGG-16 + VGG-M

84.1%

0.026

Compact Bilinear CNN [25]

n/a

n/a

8,192

VGG-16

84.0%

0.820

PD + SWFV [14]

n/a

n/a

69,632

VGG-16

84.5%

0.097

SCDA [13]

n/a

n/a

4,096

VGG-16

80.5%

1.572

Ours

n/a

n/a

69,992

VGG-16

86.4%

0.099

Ours (Compact vector)

n/a

n/a

4,096

VGG-16

84.5%

1.650

  

Dataset: air

    

Symbolic [5]

BBox

BBox

20,992

Shallow feature: SIFT

72.5%

0.276

Re-Fisher Vector [34]

n/a

n/a

655,360

Shallow feature: SIFT

81.5%

0.001

Bilinear CNN [8]

n/a

n/a

262,144

VGG-16 + VGG-M

83.9%

0.0256

Ours (Full Vector + MI 2)

n/a

n/a

69,992

VGG-16

87.7%

0.100

Ours (Compact vector)

n/a

n/a

4,096

VGG-16

82.5%

1.611

  

Dataset: cars

    

Symbolic [5]

BBox

BBox

20,992

Shallow feature: SIFT

78.0%

0.297

PG-Alignment [4]

BBox

n/a

126,976

VGG-19

92.6%

0.058

Re-Fisher Vector [34]

n/a

n/a

655,360

Shallow feature: SIFT

82.7%

0.011

Bilinear CNN [8]

n/a

n/a

262,144

VGG-16 + VGG-M

91.3%

0.028

Ours

n/a

n/a

69,992

VGG-16

92.4%

0.106

Ours (Compact vector)

n/a

n/a

4,096

VGG-16

87.5%

1.709

  

Dataset: dogs

    

Symbolic [5]

BBox

BBox

20,992

Shallow feature: SIFT

45.6%

0.174

Selective Pooling [35]

BBox

BBox

163,840

Shallow feature: SIFT

52.0%

0.025

Re-Fisher Vector [34]

n/a

n/a

327,680

Shallow feature: SIFT

52.9%

0.013

NAC[36]

n/a

n/a

4,096

Alex-Net

68.6%

1.340

PD + SWFV [14]

n/a

n/a

36,864

Alex-Net

71.9%

0.156

Ours

n/a

n/a

40,000

Alex-Net

72.6%

0.145

Ours (Compact vector)

n/a

n/a

4,096

Alex-Net

68.4%

1.335

The 'n/a' entries in the table means that bounding box or part annotation is not used

Notes

Declarations

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
School of Electronic Information, Wuhan University
(2)
School of Remote Sensing and Information Engineering, Wuhan University

Reference

  1. W Zhang, J Yan, W Shi, T Feng, D Deng, Refining deep convolutional features for improving fine-grained image recognition. EURASIP Journal on Image and Video Processing 2017(1), 27 (2017)View ArticleGoogle Scholar

Copyright

© The Author(s). 2017