RBFD：一种鲁棒的图像局部二值特征描述子

摘要

The traditional floating feature descriptors are in high memory load and slow in matching. To best address these problems, this paper proposed a novel binary feature descriptor based on gradient statistic information comparison. Firstly, the image patch around the keypoint is divided into sub-regions, and our binary descriptor is constructed by comparing the gradient statistic information of these sub-regions. Then, a multi-gridding and multi-support region strategy is applied to boost the discrimination of our descriptor. Finally, a simplified AdaBoost algorithm is applied to realize the descriptor dimension reduction. The experimental results show that our descriptor is both efficient in construction and robust to compare with the state-of-the-art methods.%针对传统浮点型特征描述子占用空间大、匹配速度慢的问题，提出一种基于梯度统计信息比较的局部二值特征描述子。通过对比特征点邻域梯度统计信息生成二值特征描述子，再利用多邻域和多分块策略提高描述子判别力，最后通过近似简化的 AdaBoost 算法实现描述子降维。实验结果表明，与已有描述子相比，文中提出的描述子在实现快速生成的同时其鲁棒性更强。