面向草图检索的小样本增量有偏学习算法

Small Sample Incremental Biased Learning Algorithm for Sketch Retrieval

为了解决草图检索相关反馈中小样本训练、数据不对称及实时性要求这3个难点问题,提出了一种小样本增量有偏学习算法.该算法将主动式学习、有偏分类和增量学习结合起来,对相关反馈过程中的小样本有偏学习问题进行建模.其中,主动式学习通过不确定性采样,选择最佳的用户标注样本,实现有限训练样本条件下分类器泛化能力的最大化;有偏分类通过构造超球面区别对待正例和反例,准确挖掘用户目标类别;每次反馈循环中新加入的样本则用于分类器的增量学习,在减少分类器训练时间的同时积累样本信息,进一步缓解小样本问题.实验结果表明,该算法可以有效地改善草图检索性能,也适用于图像检索和三维模型检索等应用领域.

This paper proposes an algorithm named Small Sample Incremental Biased Learning Algorithm to solve three difficulties of relevance feedback in sketch retrieval, including small sample issue, asymmetry of training data and real-time requirement. The algorithm combines active learning, biased classification and incremental learning to model the small sample biased learning problem in relevance feedback process. Active learning employs uncertainty sampling to choose the best.labeling samples, so that the generalization ability of classifier is maximized with the limited training data; Biased classification constructs hyperspheres to treat positive and negative data differently, which distinguishes the user＇s target class accurately; Newly labeled samples in each feedback loop are used to train the classifier incrementally to reduce the training time. Incremental learning also collects training data to further alleviate the small sample problem. Experimental results show that this algorithm improves the performance of sketch retrieval. And it can be well extended to other retrieval domains like CBIR （content based image retrieval）, 3D retrieval, and so on.