中文摘要：

针对传统基于SVM分类器的多核学习方法优化参数多、优化过程复杂、计算量大的缺点,本文提出基于Real Adaboost的多核学习方法解决通用目标分类与识别问题。该方法根据核函数能将高维特征映射到低维空间的特性,采用核函数空间上的线性平面分割构建弱分类器,并用Real Adaboost学习框架对弱分类器进行学习。先用分层特征算子PHOG和PHOW分别提取图像不同尺度的形状和表观信息,并用核函数计算特征距离,然后在核空间上构建线性弱学习器池,最后用Real Adaboost算法学习得到强分类器。实验结果表明,该方法有效提高了图像分类的准确率。

英文摘要：

Kernel function is a normal method for image categorization to map high-dimension features into low-dimension spaces.Most state-of-art researches integrated kernels into Support Vector Machine （SVM） classifiers to solve classification problems.A novel Real Adaboost framework is proposed to involve kernel method to deal with classification.Hierarchical features PHOG and PHOW are used to describe shape and appearance information in multiple image scales first.Kernel function is then employed for evaluating features＇ distance and constructing linear learner pool in kernel space.Real Adaboost is finally used to linear learners to obtain final image classifier.Experimental results show that our method significantly improves the image categorization performance.