针对学习算法不能很好地适用于由不等势(unequal cardinality)且无序的特征点集组成的实例，提出密度导向的树型结构核函数。将特征点集自适应分解成一个树型结构，把两个特征点集嵌入该树型结构，形成两个多分辨率直方图。然后计算由这两个多分辨率直方图公共节点所含特征点的密度加权的交叉函数值。该核函数可以自适应确定特征点集之间的局部对应(partial corresponding)关系，具有与特征点数成线性关系的计算复杂度，且是正定的。将该核函数嵌入基于核的判别分类器进行图像目标分类，并与词汇导向的金字塔匹配核进行比较，实验结果表明，密度导向的树型结构核函数能获得较好的分类性能。

Density-guided tree-structured kernel is proposed for the situation that the learning algorithm is not fit well for instances consisting of unordered feature sets with unequal cardinality.The feature set is automatically decomposed into a tree，and two feature sets are embedded into this tree to form two multi-resolution histograms，and then the histogram intersection，weighted by the density of feature points in common nodes from two multi-resolution histograms，is computed.The partial correspondences between feature sets can be determined automatically through this kernel，its computation is linear with the number of features，and it is positive-define.This kernel is embedded into kernel based discriminative classifier for image object classification，and compared with vocabulary-guided pyramid match kernel.The experiments show that the density-guided tree-structured kernel can obtain the better classification performance.