一种基于近邻保留的相关反馈图像检索算法

Neighborhood Preserving-based Relevance Feedback Algorithm in Image Retrieval

图像检索中很多时候会出现相关反馈提供的标注样本数不足,从而导致监督学习方法面临过适应问题的困扰。提出一种能有效使用未标记数据的半监督新型算法:近邻保留回归算法,它通过使已标记数据的观测误差函数最小化,来选择综合性能最好的回归函数,以兼顾图像的语义特征及图像空间的几何结构,并解决过适应问题。实验结果证明,算法能有效提高图像检索系统的性能。

When there are no sufficient feedback samples provided by Relevance feedback,supervised learning methods may suffer from the over-fitting in image retrieval.This paper proposed a novel neighborhood preserving regression algorithm which makes efficient use of unlabeled images.The algorithm selects the function which can minimize the empirical loss on the labeled images,thus,the function can respect both semantic and geometrical structures of the image database.The experimental results show that the algorithm is effective for image retrieval.