AdaBoost算法的训练集
如何找到AdaBoost算法的Haar特征的负训练数据集和正训练数据集？假设您想要在图像中定位某种类型的斑点，并且整个数组中有多个斑点 - 您如何开始训练它？我希望尽可能得到非技术性的解释。
How do you find the negative and positive training data sets of Haar features for the AdaBoost algorithm? So say you have a certain type of blob that you want to locate in an image and there are several of them in your entire array - how do you go about training it? I'd appreciate a nontechnical explanation as much as possible.
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首先，AdaBoost 不一定与 Haar 特性有任何关系。 AdaBoost 是一种将弱学习器组合成强学习器的学习算法。 Haar 特征只是 AdaBoost 算法可以学习的一种数据。
其次，获取它们的最佳方法是预先安排数据。因此，如果您想像 Viola 和 Jones 那样进行面部识别，您将想要在蒙版/覆盖图像中标记图像中的面部。训练时，您从图像中选择样本，以及选择的样本是正样本还是负样本。这种积极性/消极性来自于您之前在图像中对脸部（或其他内容）的标记。
您必须自己进行实际实施，但您可以使用现有项目指导你，或者你可以修改他们的项目。
First, AdaBoost does not necessarily have anything to do with Haar features. AdaBoost is a learning algorithm that combines weak learners to form a strong learner. Haar features are just a type of data on which an AdaBoost algorithm can learn.
Second, the best way to get them is to prearrange your data. So, if you want to do facial recognition a la Viola and Jones, you'll want to mark the faces in your images in a mask/overlay image. When you're training, you select samples from the image, as well as whether the sample you select is positive or negative. That positivity/negativity comes from your previous marking of the face (or whatever) in the image.
You'll have to make the actual implementation yourself, but you can use existing projects to either guide you, or you can modify their projects.