Semi-supervised Feedback Algorithm Based on Locally Adaptive Approximation
基于局部自适应逼近的半监督反馈算法

In this paper, identification information was put into the distance measure, using this new distance measure instead of the Euclidean distance to construct k-neighbor, we proposed a new local linear nearest neighborhood propagation method. I}his provides a semi-supervised feedback algorithm based on the local adaptive approximation for image retrieval relevance feedback mechanism FLANNP (feedback locally adaptive nearest neighbor propagation). The decision function constructed by SVMs was used to determine the most discriminant direction in a neighborhood around the query. Such a direction provides a local adaptive distance algorithm. 13y this the reconstruction weights were computed.After all the labels were propagated from the labeled points to the whole dataset using the local linear neighborhoods with sufficient smoothness. The approach makes use of identification information in distance measure and improves the accuracy of image retrieval.