Abstract
As the amount of visual information is rapidly increasing, users want to find the more semantic information easily. Most retrieval systems by low-level features(such as color, texture) could not satisfy user’s demand. To interpret semantic of image, many researchers use keywords as textual annotation. However, it’s the image retrieval without ranking by text matching which is the simplest way to retrieval according to keyword’s existence or nonexistence. In this paper, we propose conceptualization by similarity measure using relations among keywords for efficient image retrieval. We experiment annotated image retrieval by lowering the unrelated keyword’s weight value and raising important keyword’s one.
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Resnik, P.: Using information content to evaluate semantic similarity. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp. 448–453, Montreal (1995)
Leacock, C., Chodorow, M.: Combining local context and WordNet similarity for word sense identification. In: Fellbaum, C. (ed.) WordNet: An electronic lexical database, pp. 265–283. The MIT Press, Cambridge, MA (1998)
Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning. Madison, WI (1998)
Hirst, G., St-Onge, D.: Lexical Chains as representations of context for the detection and correction of malapropisms. In: Fellbaum, C. (ed.) WordNet: An electronic lexical database, pp. 305–332. The MIT Press, Cambridge, MA (1998)
Resnik, P.: Semantic Similarity in a Taxonomy: An Information-Based Measure and its Applications to Problems of Ambiguity in Natural Language. Journal of Artificial Intelligence Research 11, 95–130 (1999)
Banerjee, S., Pedersen, T.: Extended Gloss Overlaps as a Measure of Semantic Relatedness. In: The Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, Acapulco, Mexico (2003)
Patwardhan, S., Pedersen, T.: Using WordNet-based Context Vectors to Estimate the Semantic Relatedness of Concepts. In: The Proceedings of the EACL 2006 Workshop Making Sense of Sense - Bringing Computational Linguistics and Psycholinguistics Together. Trento, Italy (2006)
Yohan, J., Latifur, K., Lei, W., Mamoun, A.: Image Annotation by Combining Multiple Evidence & WordNet. In: Proceedings of the 13th annual ACM international conference on Multimedia table of contents, Hilton, Singapore (2005)
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Cho, M., Choi, C., Kim, H., Shin, J., Kim, P. (2007). Efficient Image Retrieval Using Conceptualization of Annotated Images. In: Sebe, N., Liu, Y., Zhuang, Y., Huang, T.S. (eds) Multimedia Content Analysis and Mining. MCAM 2007. Lecture Notes in Computer Science, vol 4577. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73417-8_51
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DOI: https://doi.org/10.1007/978-3-540-73417-8_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73416-1
Online ISBN: 978-3-540-73417-8
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