Correlated Probabilistic Label Propagation for Region-Based Image Retrieval

Label propagation and manifold ranking have been successfully adopted in content-based image retrieval (CBIR) in recent years. However, while the global low-level features are widely utilized in current systems, region-based features have received little attention. In this paper, a novel transductive framework based on correlated probabilistic label propagation is proposed for region-based images retrieval (RBIR), which can be characterized by three key properties: (1) unified feature matching (UFM) is chosen to measure the similarity between two segmented images. (2) To represent the segmented images in a uniform feature space, a generative model is adopted and the probabilistic labels of each image can be obtained. (3) In the retrieval process, multiple probabilistic labels of training samples are propagated simultaneously on the weighted graph, and the correlation among different labels are explored. Experimental results on 10000 images show that our algorithm can greatly improve the retrieval performance of the RBIR system.