Skip to main content

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

The Original Article was published on 08 April 2017

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

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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dexiang Deng.

Additional information

The online version of the original article can be found under doi:10.1186/s13640-017-0176-3.

Rights and permissions

Open Access This 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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, W., Yan, J., Shi, W. et al. Erratum to: Refining deep convolutional features for improving fine-grained image recognition. J Image Video Proc. 2017, 33 (2017). https://doi.org/10.1186/s13640-017-0183-4

Download citation

  • Received:

  • Accepted:

  • Published:

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