SIA: semantic image annotation using ontologies and image content analysis

Authors:
Pyrros Koletsis;Euripides G. M. Petrakis
Affiliations:
Department of Electronic and Computer Engineering, Technical University of Crete (TUC), Chania, Greece;Department of Electronic and Computer Engineering, Technical University of Crete (TUC), Chania, Greece
Venue:
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Year:
2010

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Abstract

We introduce SIA, a framework for annotating images automatically using ontologies. An ontology is constructed holding characteristics from multiple information sources including text descriptions and low-level image features. Image annotation is implemented as a retrieval process by comparing an input (query) image with representative images of all classes. Handling uncertainty in class descriptions is a distinctive feature of SIA. Average Retrieval Rank (AVR) is applied to compute the likelihood of the input image to belong to each one of the ontology classes. Evaluation results of the method are realized using images of 30 dog breeds collected from the Web. The results demonstrated that almost 89% of the test images are correctly annotated (i.e., the method identified their class correctly).