For the image retrieval task which combines machine learning theory with relevance feedback mechanism

this paper focuses on the graph-based semi-supervised learning algorithm with application to region-based image retrieval.Different schemes which both incorporate the region saliency into the graph-based semi-supervised learning framework are applied to deal with two types of feedback.Firstly

in the case that no sample or only positive samples are available from the user's feedback

the retrieval task can be resolved via a transductive learning manner

a hierarchical graph model which incorporates region saliency information is constructed and the manifold-ranking algorithm is adopted subsequently for positive label propagation.Secondly

in the case that the user provides both positive and negative samples

the region-level adjacency matrix will be constructed via the feedback samples

and the manifold-ranking algorithm is also adopted here to choose instances which truly represent the user's query semantics.The selected instances are then used to retrieve the relevant samples.The experiments have proved the effectiveness of the proposed method.