engleski

Multi-layered Image Representation for Image Interpretation

In order to bridge the semantic gap between the visual context of an image and semantic concepts people would use to interpret it, we propose a multi-layered image representation model considering different amounts of knowledge needed for the interpretation of the image at each layer. Interpretation results on different semantic layers of Corel images related to outdoor scenes are presented and compared. Obtained results show positive correlation of precision and recall with the abstract level of classes used for image annotation, i.e. more generalized classes have achieved better results.

image annotation; petri net; knowledge representation scheme; bayesian classifier

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