关键词: 图像语义分类, 二叉树, 支持向量机

Abstract: In order to decrease the “semantic gap” between low-level visual features and high-level semantics, a novel method of low-level visual features mapping high-level semantics is proposed, which uses a tree structure Support Vector Machine（SVM）. Binary decision tree is utilized to design SVM classifiers and distance measure is used to realize clustering at the kernel space. Binary decision tree architecture can shorten the time of semantic classification so much, while separating the semantic classes which have bigger distance can promise high classification accuracy. The experimental results show that proposed method not only can promise high accuracy, but also shorten retrieval time so much.

Key words: image semantic classification, binary decision tree, Support Vector Machine（SVM）