Özdemir, BahadırAksoy, Selim2016-02-082016-02-0820101051-4651http://hdl.handle.net/11693/28512Date of Conference: 23-26 Aug. 2010We 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.EnglishBag of wordsData setsFrequent subgraph miningHigh level semanticsImage representationsInterest regionsSpatial arrangementsStatistical classificationSubgraphsVisual wordGraphic methodsPattern recognitionSupport vector machinesVector spacesStatistical methodsConference Paper10.1109/ICPR.2010.278