图像检索中基于最大信息获取量的主动学习算法

本文提出一种基于内容的图像中的主动学习算法. 首先用支撑向量机学习得到初始查询概念, 然后用相似性测度对其进行检验, 选取信息量最大的样本来请求用户标记, 最后在相关反馈的迭代优化过程中获取用户的图像查询概念. 算法通过支撑向量机二值分类器与相似性测度2种不同学习模型的融合, 来减轻它们各自所存在的模型偏置. 实验结果显示, 所提算法能够显著提高图像检索的精确度, 在少量的反馈迭代之后即能准确地获取目标概念.

Active learning based on maximizing information gain for content-based image retrieval

This paper describes a new method for active learning in content-based image retrieval. The proposed method firstly uses support vector machine (SVM) classifiers to learn an initial query concept. Then the proposed active learning scheme employs similarity measure to check the current version space and selects images with maximum expected information gain to solicit user's label. Finally, the learned query is refined based on the user's further feedback. With the combination of SVM classifier and similarity measure, the proposed method can alleviate model bias existing in each of them. Our experiments on several query concepts show that the proposed method can learn the user's query concept quickly and effectively only with several iterations.