Abstract
Automating the process of annotation of images is a crucial step towards efficient and effective management of increasingly high volume of content. A graph-based approach for automatic image annotation is proposed which models both feature similarities and semantic relations in a single graph. The proposed approach models the relationship between the images and words by an undirected graph. Semantic information is extracted from paired nodes. The quality of annotation is enhanced by introducing graph link weighting techniques. The proposed method achieves fast solution by using incremental fast random walk with restart (IFRWR) algorithm, without apparently affecting the accuracy of image annotation.
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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Aishwaryameenakshi, K., Halima Banu, S., Krishna Priya, A.T.R., Chitrakala, S. (2012). Graph Learning System for Automatic Image Annotation. In: Das, V.V., Stephen, J. (eds) Advances in Communication, Network, and Computing. CNC 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 108. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35615-5_65
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DOI: https://doi.org/10.1007/978-3-642-35615-5_65
Publisher Name: Springer, Berlin, Heidelberg
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