中文摘要：

为了提高视觉特征的鲁棒性，提出尺度空间下最稳定极值区域特征的挖掘算法．该算法在尺度空间下检测特征，首先通过分析特征的局部稳定性来挖掘鲁棒的特征，然后对区分力低的特征基于熵值进行过滤；在此基础上，基于尺度空间下最稳定极值区域特征的尺度一致性提出了基于最大匹配尺度的检索算法，可提高局部敏感哈希高维索引的检索性能．与多种已有方法进行比较的结果表明，文中算法的平均检索精度相对提高10％以上，查询效率也有提升．

英文摘要：

To improve the robustness of visual features, we propose a feature mining algorithm of maximally stable extremal regions in scale space. The algorithm detects features in scale space, mines the robust features by analyzing local stability, and then filters features with low discriminability according to entropy; on that basis, by exploiting scale consistency of maximally stable extremal regions in scale space, we propose an efficient retrieval algorithm based on the maximally matched scale, which can improve the retrieval performance of locality sensitive hashing high-dimensional index. Experimental results show that the precision of our algorithm is 10~ higher on average than existing methods while the efficiency is also improved.