Summary
Due to the importance of semantic meaning in image retrieval, manual or semi-automated annotation still remains indispensable in both professional and personal retrieval applications. Annotations are used to facilitate textual or conceptual queries in large image repositories and thus to classify the image data into semantic classes. However, different users’ perception of image contents and the lack of standards among different annotation tools make it necessary to develop methods for the unification and integration of different annotation schemes. In this chapter we present a graph approach as a representation technique for the complex semantic annotation space which is generated by the transformation of the subjective perceptions into a unified knowledge base. Our technique bridges the discrepancy between users’ vocabulary and the several levels of abstraction at which content descriptions are assigned. Based on examples, we show how to integrate our method into probabilistic approaches to (semi-) automatic image annotation.
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Vompras, J., Conrad, S. (2008). Management and Processing of Personalized Annotations in Image Retrieval Systems. In: Wallace, M., Angelides, M.C., Mylonas, P. (eds) Advances in Semantic Media Adaptation and Personalization. Studies in Computational Intelligence, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76361_7
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DOI: https://doi.org/10.1007/978-3-540-76361_7
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