一种基于随机森林的特征匹配方法

Feature Matching Method Based on Random Forests Classifier

在特征匹配问题中,匹配速度与精度常常难以同时保证。为了解决该问题,本文提出一种基于随机森林的特征匹配算法。结合SUSurE算法,在尺度空间下提取局部不变特征,构建训练样本集合,对随机森林进行离线训练建立分类模型。在实时匹配中,选取候选特征点对其进行实时分类,完成特征匹配,并与SIFT算法在不同尺度、旋转、视角方面等进行实验对比。结果表明,本文算法在具有良好的实时性情况下,仍有较高的光照适应性和匹配精度。

It is difficult to be speedy as well as accurate in feature matching. To overcome the drawback, this paper proposes a feature matching method based on random forest. This method extracts local invariant features in scale space by SUSurE algo- rithm, then the features and its adjacent pixels are constructed as training samples. In off-line, the random forests are trained and a classification model is acquired to deal with the scale, rotation, illumination and perspective changes. In the online stage, the candidate feature points input RF classifier for real-time classification and feature matching. Comparative tests are made between our approach and SIFT. Experimental results show that the method based on RF is generally more robust and faster in the premise of real-time, and is good at accuracy, as well as adjusting to the illumination changes.