检索从 SIFT / SURF 获得的最重要特征
我正在使用 SURF 从图像中提取特征并将其与其他图像进行匹配。我的问题是，某些图像具有超过 20000 个特征，这会减慢爬行匹配速度。
有没有办法可以从该集中提取 n 个最重要的特征？
我尝试计算图像的 MSER，并且仅使用这些区域内的特征。这使我的工作量减少了 5% 到 40%，且不会对匹配质量产生负面影响，但这是不可靠的，而且仍然不够。
我还可以缩小图像的大小，但在某些情况下，这似乎会严重影响特征的质量。
SURF 提供了一些参数（粗麻布阈值、八度音阶和每八度音阶的层数），但我找不到任何有关更改这些参数将如何影响特征重要性的信息。
I'm using SURF to extract features from images and match them to others. My Problem is that some images have in excess of 20000 features which slows down matching to a crawl.
Is there a way I can extract only the n most significant features from that set?
I tried computing MSER for the image and only use features that are within those regions. That gives me a reduction anywhere from 5% to 40% without affecting matching quality negatively, but that's unreliable and still not enough.
I could additionally size the image down, but I that seems to affect the quality of features severely in some cases.
SURF offers a few parameters (hessian threshold, octaves and layers per octave) but I couldn't find anything on how changing these would affect feature significance.
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经过一些研究和测试，我发现每个特征的 Hessian 值是对其强度的粗略估计，但是使用按 Hessian 排序的前 n 个特征并不是最佳选择。
当执行以下操作时，我取得了更好的结果，直到特征数量低于 n 的目标：
现在我只需要找到一个合适的n，但目前1500左右似乎就足够了。
After some researching and testing I have found that the Hessian value for each feature is a rough estimate of it's strength, however using the top n features sorted by the hessian is not optimal.
I achieved better results when doing the following until number of features is below the target of n:
Now I only need to find a suitable n, but around 1500 seems enough currently.