Abstract
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.
Cite
CITATION STYLE
Ivasic-Kos, M., Pobar, M., & Ipsic, I. (2014). Multi-layered Image Representation for Image Interpretation. In V and L Net 2014 - 3rd Annual Meeting of the EPSRC Network on Vision and Language and 1st Technical Meeting of the European Network on Integrating Vision and Language, A Workshop of the 25th International Conference on Computational Linguistics, COLING 2014 - Proceedings (pp. 115–117). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/w14-5419
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