一种用于图像检索的新型半监督学习算法

基于支持向量机的理论提出了一种用于图像检索的半监督学习算法。该算法的基本思想是,如果两点彼此是最近点,则它们共用一个标注。因此,该算法可以在具有最大类间空隙和很好保留位置特征的基础上找到一个投影。对该算法和标准支持向量机及转导(transductive)支持向量机的图像检索效果进行了实验比较,结果表明该算法可以获得更好的效果。

Algorithm for Semi-Supervised Learning in Image Retrieval

In this paper, based on Support Vector Machine (SVM), we introduce a semi-supervised learning algorithm for image retrieval. The basic consideration of the algorithm is that, if two data points are close to each other, they should share the same label. Therefore, it is reasonable to search a projection with maximal margin and locality preserving property. Comparing our algorithm to standard SVM and transductive SVM, Experimental results show efficiency and effectiveness of our algorithm.