一种新的直方图核函数及在图像分类中的应用

基于核方法的支持向量机(SVM)以其良好的推广性在图像分类等领域已经得到广泛应用，运用支持向量机的关键是设计有效的核函数。为克服传统核函数较少融合先验知识的弱点，该文提出基于数据驱动的核函数构建方法；并结合词包(BOW)模型，设计了一种基于TF-IDF规则的加权二次卡方(Weighted Quadritic Chisquared, WQC)距离的直方图核函数；在计算直方图之间距离时充分考虑到不同量化区间的不同区分性能，从而增强核函数对不同类别的区分能力。在Caltech101/256等多个经典图像数据集上的分类实验结果验证了该文方法的有效性。

A New Histogram-based Kernel Function Designed for Image Classification

Kernel-based Support Vector Machine(SVM) is widely used in many fields(e.g.image classification) for its good generalization,in which the key factor is to design effective kernel functions.As there is not much a priori knowledge introduced into traditional kernel functions,the data-driven kernel building method is proposed to construct a new histogram kernel function which is combined with Bag OF Word(BOW) model and based on TF-IDF Weighted Quadratic Chi-squared(WQC) distance.In the process of calculating d...