Multilabel Neighborhood Propagation for Region-Based Image Retrieval
Content-based image retrieval (CBIR) has been an active research topic in the last decade. As one of the promising approaches, graph-based semi-supervised learning has attracted many researchers. However, while the related work mainly focused on global visual features, little attention has been paid to region-based image retrieval (RBIR). In this paper, a framework based on multilabel neighborhood propagation is proposed for RBIR, which can be characterized by three key properties: 1) For graph construction, in order to determine the edge weights robustly and automatically, mixture distribution is introduced into the earth mover's distance (EMD) and a linear programming framework is involved. 2) Multiple low-level labels for each image can be obtained based on a generative model, and the correlations among different labels are explored when the labels are propagated simultaneously on the weighted graph. 3) By introducing multilayer semantic representation (MSR) and support vector machine (SVM) into the long-term learning, more exact weighted graph for label propagation and more meaningful high-level labels to describe the images can be calculated. Experimental results, including comparisons with the state-of-the-art retrieval systems, demonstrate the effectiveness of our proposal.
region-based image retrieval semi-supervised learning label propagation manifold ranking relevance feedback