Image Retrieval via Probabilistic Hypergraph Ranking

In this paper, we propose a new transductive learning framework for image retrieval, in which images are taken as vertices in a weighted hypergraph and the task of image search is formulated as the problem of hypergraph ranking. Based on the similarity matrix computed from various feature descriptors, we take each image as a ‘centroid’ vertex and form a hyperedge by a centroid and its k-nearest neighbors. To further exploit the correlation information among images, we propose a probabilistic hypergraph, which assigns each vertex vi to a hyperedge ej in a probabilistic way. In the incidence structure of a probabilistic hypergraph, we describe both the higher order grouping information and the affinity relationship between vertices within each hyperedge. After feedback images are provided, our retrieval system ranks image labels by a transductive inference approach, which tends to assign the same label to vertices that share many incidental hyperedges, with the constraints that pre...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Yuchi Huang, Qingshan Liu, Shaoting Zhang, Metaxas Dimitris
• Keywords: Computer Vision, CVPR 2010, Images, Probabilistic Hypergraph, Weighted Hypergraph