通过设计一种全新的包与包之间的相似性度量方法,即混合型Hausdorff距离,改进了Citation-KNN这一经典多示例算法；并通过针对林木自身特殊的成像特点,分析了林木类图像处理的难点,并利用基于小波域变换的处理技术,提出了专门的林木图像特征生成方法,使改进后的算法可以有效实现对林木种类的识别,进而成功将多示例学习引入了林木分类领域.实验证明:新算法不仅对林木分类领域问题的实现效果最佳,同时对公认数据集的测试也取得了良好的结果,与目前主流算法高度可比.%A new method, named mix-Hausdorf distance, was designed to measure the similarity a-mongst packages. It can improve the performance of a classic multi-instance algorithm called Citation-KNN. According to trees' distinct imaging features, the paper analyzed the difficulty of handling trees graphs. Using wavelet transformation handling technique, a specialized method for generating the feature vector of tree images was proposed in order to make the improved algorithm identify the trees effectively. Our experiment proved that, compared with the main existing algorithms, the proposed algorithm has best effect for trees classification and good results when testing the recognized dataset.
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