一种基于层次语义图像分类的改进方法

摘要

为了解决层次语义图像中分类率低，特别是高层语义图像分类率低的问题，采用两种解决措施。首先引入Fuzzy Support Vector Machine （ FSVM）理论，并对FSVM做出改进，消除由Support Vector Machine （ SVM）构成的多类分类器中的不可分区域，从而使低层语义图像分类准确率提升，为高层语义分类提供基础。然后再建立底层图像特征与低层语义图像之间的映射关系，对低层语义的图像做高层语义上的关联，最终实现层次化的语义描述结构。实验表明，所提出的方法提高了层次语义图像，特别是高层语义图像分类准确率。%This paper uses two solutions for the problem of low classification rate in hierarchical semantic images , in particular the high-level hierarchical semantic images .Firstly we introduce the theory of fuzzy support vector machine ( FSVM ) and improve it , this eliminates the unclassifiable region of the multi-class classifiers constructed with support vector machine ( SVM ) , therefore the image classification accuracy rate of lower-level semantic images is enhanced;it provides a basis for the high-level semantic classification .Then, we establish the mapping relationship between the bottom image characteristics and the lower-level semantic images for making the association of high-level semantics for low-level semantic image , and finally achieve the hierarchical semantic description structure .Experimental results show that the presented method can improve the classification accuracy rate of hierarchical semantic images , especially of the high-level hierarchical semantic images .