Computer Vision – ECCV 2010

Volume 6313 of the series Lecture Notes in Computer Science pp 663-676

Critical Nets and Beta-Stable Features for Image Matching

  • Steve GuAffiliated withDepartment of Computer Science, Duke University
  • , Ying ZhengAffiliated withDepartment of Computer Science, Duke University
  • , Carlo TomasiAffiliated withDepartment of Computer Science, Duke University

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We propose new ideas and efficient algorithms towards bridging the gap between bag-of-features and constellation descriptors for image matching. Specifically, we show how to compute connections between local image features in the form of a critical net whose construction is repeatable across changes of viewing conditions or scene configuration. Arcs of the net provide a more reliable frame of reference than individual features do for the purpose of invariance. In addition, regions associated with either small stars or loops in the critical net can be used as parts for recognition or retrieval, and subgraphs of the critical net that are matched across images exhibit common structures shared by different images. We also introduce the notion of beta-stable features, a variation on the notion of feature lifetime from the literature of scale space. Our experiments show that arc-based SIFT-like descriptors of beta-stable features are more repeatable and more accurate than competing descriptors. We also provide anecdotal evidence of the usefulness of image parts and of the structures that are found to be common across images.