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- W66342537 abstract "Automaticcontentextraction,classiflcationandcontent-basedretrievalarehighly desired goals in intelligent remote sensing databases. Pixel level processing has been the common choice for both academic and commercial systems. We extend the modeling of remotely sensed imagery to three levels: Pixel level, region level andscenelevel.Pixellevelfeaturesaregeneratedusingunsupervisedclusteringof spectral values, texture features and ancillary data like digital elevation models. Regionlevelfeaturesincludeshapeinformationandstatisticsofpixellevelfeature values.Scenelevelfeaturesincludestatisticsandspatialrelationshipsofregions. This chapter describes our work on developing a probabilistic visual grammar to reduce the gap between low-level features and high-level user semantics, and to support complex query scenarios that consist of many regions with difierent feature characteristics. The visual grammar includes automatic identiflcation of region prototypes and modeling of their spatial relationships. The system learns the prototype regions in an image collection using unsupervised clustering. Spatial relationships are represented by fuzzy membership functions. The system automatically selects signiflcant relationships from training data and builds visualgrammarmodelswhichcanalsobeupdatedusinguserrelevancefeedback.A Bayesianframeworkisusedtoautomaticallyclassifyscenesbasedonthesemodels. We demonstrate our system with query scenarios that cannot be expressed by traditional region or scene level approaches but where the visual grammar providesaccurateclassiflcations andefiective retrieval." @default.
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- W66342537 date "2003-07-01" @default.
- W66342537 modified "2023-09-25" @default.
- W66342537 title "SCENE MODELING AND IMAGE MINING WITH A VISUAL GRAMMAR" @default.
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- W66342537 doi "https://doi.org/10.1142/9789812796752_0003" @default.
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