问题描述
我是opencv的新人。我的问题是：
I'm new in opencv. My question is:
我有2个火车图像描述符trainA和trainB。
然后我构造一个向量来放置它们，并将它们添加到flann匹配器中用于训练。
I have 2 train image descriptor trainA and trainB.Then i construct a vector to put them in, and add them into flann matcher for train.
之后，我使用查询图像描述符queryC做
After that, i use query image descriptor queryC to do the knnMatch and get a DMatchs returned.
在这种情况下，哪个列车描述符将用于匹配queryC，trainA或trainB？
和训练如何帮助提高匹配精度？
In this case, which train descriptor would be used to match queryC, trainA or trainB?and how does the training help to improve the match accuracy?
提前感谢。
推荐答案
两者。它不是标准意义上的训练。 训练在这里意味着只是描述符的集合，基于哪个kd树被构建。
更简单 - 当进行匹配时，从集合S1中取一个特征，并在集合S2中寻找最近邻。 FLANN（kd-tree的实现）只是快速找到（近似）最近邻。
Both. It is not "training" in standard meaning. "Training" here means just "set of the descriptors, based on which kd-tree is build".More simple - when you do matching, you take one feature from set S1 and look for nearest neighbor in the set S2. FLANN (realization of the kd-tree) is just fast way how find (approximate) nearest neighbor.
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