Abstract
In this article, we focus our efforts (i) on the study of how to automatically extract and exploit visual concepts and (ii) on fast visual diversity. First, in the Visual Concept Detection Task (VCDT), we look at the mutual exclusion and implication relations between VCDT concepts in order to improve the automatic image annotation by Forest of Fuzzy Decision Trees (FFDTs). Second, in the ImageCLEFphoto task, we use the FFDTs learnt in VCDT task and WordNet to improve image retrieval. Third, we apply a fast visual diversity method based on space clustering to improve the cluster recall score. This study shows that there is a clear improvement, in terms of precision or cluster recall at 20, when using the visual concepts explicitly appearing in the query and that space clustering can be efficiently used to improve cluster recall.
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Arni, T., Clough, P., Sanderson, M., Grubinger, M.: Overview of the ImageCLEFphoto 2008 photographic retrieval task. In: Peters, C., et al. (eds.) CLEF 2008. LNCS, vol. 5706, pp. 500–511. Springer, Heidelberg (2009)
Deselaers, T., Deserno, T.M.: 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)
Fellbaum, C. (ed.): WordNet - An Electronic Lexical Database. Bradford Books (1998)
Marsala, C., Bouchon-Meunier, B.: Forest of fuzzy decision trees. In: Proceedings of the Seventh International Fuzzy Systems Association World Congress, vol. 1, pp. 369–374 (1997)
Marsala, C., Detyniecki, M.: Trecvid 2006: Forests of fuzzy decision trees for high-level feature extraction. In: TREC Video Retrieval Evaluation Online Proceedings (2006)
Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2008)
Tollari, S., Glotin, H.: Web image retrieval on ImagEVAL: Evidences on visualness and textualness concept dependency in fusion model. In: ACM Conference on Image and Video Retrieval (CIVR), pp. 65–72 (2007)
Tollari, S., Mulhem, P., Ferecatu, M., Glotin, H., Detyniecki, M., Gallinari, P., Sahbi, H., Zhao, Z.-Q.: A comparative study of diversity methods for hybrid text and image retrieval approaches. In: Peters, C., et al. (eds.) CLEF 2008. LNCS, vol. 5706, pp. 585–592. Springer, Heidelberg (2009)
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Tollari, S., Detyniecki, M., Fakeri-Tabrizi, A., Marsala, C., Amini, MR., Gallinari, P. (2009). Using Visual Concepts and Fast Visual Diversity to Improve Image Retrieval. 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_71
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DOI: https://doi.org/10.1007/978-3-642-04447-2_71
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