Watson Visual Recognition可以确定密度吗? [英] Can Watson Visual Recognition determine density?

问题描述

花束是我们问题领域的一个非常准确的类比.
例如，假设有三十朵花的测试图像:
-玫瑰:10
-罂粟:9
-雏菊:5
-Lillies:5
-向日葵:1

Bouquets of flowers are a fairly accurate analogy for our problem domain.
For an example, let's assume a test image of thirty flowers:
- Roses: 10
- Poppies: 9
- Daisies: 5
- Lillies: 5
- Sunflowers: 1

是否有一种训练方法可以使Watson查看花束的图片并能够以给定花朵类型的密度甚至比率或某种比率进行回复?

Is there a training approach that might get Watson to look at pictures of bouquets and be able to reply with a density of a given flower type, or even a ratio or something?

如果有什么想法，我们应该用每种花的单个/分离或多个/分组的图像进行训练吗?
...或两者的结合?

If there are any ideas, should we train with images of single/isolated or multiple/grouped of each type of flower?
...or a combination of both?

任何想法/建议都将受到欢迎！！！

ANY ideas/suggestions would be welcome!!!

另外，我们可以按动作需要进行分类，而不是按花朵类型进行分类?
但是，也许这与完全不同问题.

Alternatively, rather than making classes by flower-type, we could class by action-needed ??
But, maybe that's a different enough idead to be it's own question.

推荐答案

部分取决于您对需要分类的图像有多少控制权以及需要进行分类的粒度.例如，如果您可以确保始终从花束的顶向下视图清楚地显示所有不同的花朵，而其他无关的物体通常不在场景中，那么您可能可以针对一个分类器训练诸如五个密度级别的每种花的类型.例如，雏菊分类器将具有五个类别:0至20％的雏菊，20至40％的雏菊，40至60％的雏菊，60至80％的雏菊和80％以上的雏菊.

In part, it depends on how much control you have over the images that you need to classify, and the granularity of the classification that you need to make. If, for example you're guaranteed to always have a top down view of the bouquet that shows all the different flowers clearly and other extraneous objects are generally not in the scene, then you probably could train a classifier for something like five density levels for each flower type. For example, the Daisy classifier would have five classes: 0 to 20% daisies, 20 to 40% daisies, 40 to 60% daisies, 60 to 80% and over 80% daisies.

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