Abstract
This paper describes an application of statistical co-occurrence techniques that built on top of a probabilistic image annotation framework is able to increase the precision of an image annotation system. We observe that probabilistic image analysis by itself is not enough to describe the rich semantics of an image. Our hypothesis is that more accurate annotations can be produced by introducing additional knowledge in the form of statistical co-occurrence of terms. This is provided by the context of images that otherwise independent keyword generation would miss. We applied our algorithm to the dataset provided by ImageCLEF 2008 for the Visual Concept Detection Task (VCDT). Our algorithm not only obtained better results but also it appeared in the top quartile of all methods submitted in ImageCLEF 2008.
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References
Yavlinsky, A., Schofield, E., Rüger, S.: Automated image annotation using global features and robust nonparametric density estimation. In: Proceedings of the International ACM Conference on Image and Video Retrieval, pp. 507–517 (2005)
Hanbury, A., Serra, J.: Mathematical morphology in the CIELAB space. Image Analysis & Stereology 21, 201–206 (2002)
Tamura, H., Mori, T., Yamawaki, T.: Textural features corresponding to visual perception. IEEE Transactions on Systems, Man and Cybernetics 8(6), 460–473 (1978)
Deselaers, T., Hanbury, A.: The visual concept detection task in ImageCLEF 2008. In: Peters, C., et al. (eds.) CLEF 2008. LNCS, vol. 5706, pp. 531–538. Springer, Heidelberg (2009)
Pedersen, Banerjee, Patwardhan: Maximizing semantic relatedness to perform word sense disambiguation. Technical report, University of Minnesota (2003)
Gracia, J., Mena, E.: Web-based measure of semantic relatedness. In: Bailey, J., Maier, D., Schewe, K.-D., Thalheim, B., Wang, X.S. (eds.) WISE 2008. LNCS, vol. 5175, pp. 136–150. Springer, Heidelberg (2008)
Miller, G.A., Charles, W.G.: Contextual correlates of semantic similarity. Journal of Language and Cognitive Processes 6, 1–28 (1991)
Manning, C.D., Schütze, H.: Foundations of statistical natural language processing. MIT Press, Cambridge (1999)
Llorente, A., Rüger, S.: Can a probabilistic image annotation system be improved using a co-occurrence approach? In: Workshop on Cross-Media Information Analysis, Extraction and Management at the 3rd International Conference on Semantic and Digital Media Technologies (2008)
Tollari, S., Detyniecki, M., Fakeri-Tabrizi, A., Amini, M.R., Gallinari, P.: UPMC/LIP6 at ImageCLEFphoto 2008: On the exploitation of visual concepts (VCDT). In: Evaluating Systems for Multilingual and Multimodal Information Access – 9th Workshop of the Cross-Language Evaluation Forum (2008)
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Llorente, A. et al. (2009). Exploiting Term Co-occurrence for Enhancing Automated Image Annotation. In: Peters, C., et al. Evaluating Systems for Multilingual and Multimodal Information Access. CLEF 2008. Lecture Notes in Computer Science, vol 5706. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04447-2_79
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DOI: https://doi.org/10.1007/978-3-642-04447-2_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04446-5
Online ISBN: 978-3-642-04447-2
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