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Critical Nets and Beta-Stable Features for Image Matching



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Critical Nets and Beta Stable Features for Image Matching Steve Gu and Ying Zheng and Carlo Tomasi Department of Computer Science Duke University Durham North Carolina USA 27708 steve yuanqi tomasi cs duke edu Abstract 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 Key words bag of features constellation image matching 1 Introduction Image matching enables at least tracking stereo recognition and retrieval and is therefore arguably the most important problem in computer vision A fundamental tension exists between the repeatability and distinctiveness of the features used in matching our terminology is from a recent survey 1 Features with a small image support can often be made to be repeatable in the sense that they can be found reliably in different views of the same scene Features with more extended supports are potentially more distinctive in that two large distinct regions are less likely to look like



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