Abstract
The majority of visual communication today occurs by ways of spatial groupings, plots, graphs, data renderings, photographs and video frames. However, the degree of semantics encoded in these visual representations is still quite limited. The use of icons as a form of information encoding has been explored to a much lesser extent. In this paper we describe a framework that uses a dual domain approach involving natural language text processing and global image databases to help users identify icons suitable to visually encode abstract semantic concepts.
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Garg, S., Berg, T., Mueller, K. (2011). Iconizer: A Framework to Identify and Create Effective Representations for Visual Information Encoding. In: Dickmann, L., Volkmann, G., Malaka, R., Boll, S., Krüger, A., Olivier, P. (eds) Smart Graphics. SG 2011. Lecture Notes in Computer Science, vol 6815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22571-0_7
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