Neighborhood Preserving-based Relevance Feedback Algorithm in Image Retrieval
一种基于近邻保留的相关反馈图像检索算法

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.