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Can computer vision problems benefit from structured hierarchical classification?

Overview of attention for article published in Machine Vision and Applications, May 2016
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Mentioned by

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1 X user

Citations

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Readers on

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15 Mendeley
Title
Can computer vision problems benefit from structured hierarchical classification?
Published in
Machine Vision and Applications, May 2016
DOI 10.1007/s00138-016-0763-9
Authors

Thomas Hoyoux, Antonio J. Rodríguez-Sánchez, Justus H. Piater

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 40%
Student > Master 3 20%
Student > Doctoral Student 2 13%
Student > Bachelor 1 7%
Researcher 1 7%
Other 1 7%
Unknown 1 7%
Readers by discipline Count As %
Computer Science 13 87%
Agricultural and Biological Sciences 1 7%
Unknown 1 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 June 2016.
All research outputs
#21,141,111
of 23,794,258 outputs
Outputs from Machine Vision and Applications
#490
of 551 outputs
Outputs of similar age
#255,337
of 300,415 outputs
Outputs of similar age from Machine Vision and Applications
#6
of 6 outputs
Altmetric has tracked 23,794,258 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 551 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 300,415 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.