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dc.contributor.authorÖzdemir, B.en_US
dc.contributor.authorAksoy, S.en_US
dc.date.accessioned2016-02-08T12:22:37Z
dc.date.available2016-02-08T12:22:37Z
dc.date.issued2010en_US
dc.identifier.issn10514651
dc.identifier.urihttp://hdl.handle.net/11693/28512
dc.description.abstractWe describe an image representation that combines the representational power of graphs with the efficiency of the bag-of-words model. For each image in a data set, first, a graph is constructed from local patches of interest regions and their spatial arrangements. Then, each graph is represented with a histogram of subgraphs selected using a frequent subgraph mining algorithm in the whole data. Using the subgraphs as the visual words of the bag-of-words model and transforming of the graphs into a vector space using this model enables statistical classification of images using support vector machines. Experiments using images cut from a large satellite scene show the effectiveness of the proposed representation in classification of complex types of scenes into eight high-level semantic classes. © 2010 IEEE.en_US
dc.language.isoEnglishen_US
dc.source.titleProceedings - International Conference on Pattern Recognitionen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ICPR.2010.278en_US
dc.subjectBag of wordsen_US
dc.subjectData setsen_US
dc.subjectFrequent subgraph miningen_US
dc.subjectHigh level semanticsen_US
dc.subjectImage representationsen_US
dc.subjectInterest regionsen_US
dc.subjectSpatial arrangementsen_US
dc.subjectStatistical classificationen_US
dc.subjectSubgraphsen_US
dc.subjectVisual worden_US
dc.subjectGraphic methodsen_US
dc.subjectPattern recognitionen_US
dc.subjectSupport vector machinesen_US
dc.subjectVector spacesen_US
dc.subjectStatistical methodsen_US
dc.titleImage classification using subgraph histogram representationen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Computer Engineering
dc.citation.spage1112en_US
dc.citation.epage1115en_US
dc.identifier.doi10.1109/ICPR.2010.278en_US


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