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dc.contributor.authorChum, Ondřej
dc.contributor.authorMatas, Jiří
dc.date.accessioned2012-06-19T07:28:17Z
dc.date.available2012-06-19T07:28:17Z
dc.date.issued2010-02
dc.identifier.citationOndrej Chum and Jirí Matas. Large scale discovery of spatially related images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(2):371-377, February 2010.cze
dc.identifier.urihttp://hdl.handle.net/10467/9561
dc.description.abstractWe propose a randomized data mining method that finds clusters of spatially overlapping images. The core of the method relies on the min-Hash algorithm for fast detection of pairs of images with spatial overlap, the so-called cluster seeds. The seeds are then used as visual queries to obtain clusters which are formed as transitive closures of sets of partially overlapping images that include the seed. We show that the probability of finding a seed for an image cluster rapidly increases with the size of the cluster. The properties and performance of the algorithm are demonstrated on data sets with 104, 105, and 5 ?? 106 images. The speed of the method depends on the size of the database and the number of clusters. The first stage of seed generation is close to linear for databases sizes up to approximately 234 ?? 1010 images. On a single 2.4 GHz PC, the clustering process took only 24 minutes for a standard database of more than 100,000 images, i.e., only 0.014 seconds per image.eng
dc.language.isocescze
dc.publisherIEEEcze
dc.rights© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.eng
dc.subjectminHasheng
dc.subjectimage clusteringeng
dc.subjectimage retrievaleng
dc.subjectbag of wordseng
dc.titleLarge-Scale Discovery of Spatially Related Imagescze
dc.typečlánek z elektronického periodikacze
dc.identifier.doi10.1109/TPAMI.2009.166


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