由于图像的低层可视特征与高层语义内容之间存在巨大的语义鸿沟,而基于内容的图像分类和检索准确性极大依赖低层可视特征的描述,本文提出了一种基于多超平面支持向量机的图像语义分类方法.多超平面分类器从优化问题的复杂度和运行泛化能力两方面进行研究,是最优分离超平面分类器一种显而易见的扩展.实验结果表明,本文提出的方法在图像语义分类的准确性方面要优于诸如采用色彩特征和纹理特征的支持向量机分类器的其它方法.

Considering an enormous semantic gap problem between the low-level visual features and high-level semantic information of images, and the fact that the accuracy of content-based image classification and retrieval depends greatly on the description of low-level visual features, an image semantic classification approach is proposed based on Multiple-hyperplanes Support Vector Machines (MHSVMs). The multiple-hyperplane classifier, which is investigated from the complexity of optimization problem and the generalization performance, is the explicit extension of the optimal separating hyperplanes classifier. Experimental results show that the proposed approach is more accurate in image semantic classification than other ones, such as SVMs classifier using color and textural features.