Abstract
In this chapter, we introduce an MPEG-7 friendly system to extract semantics of aerial image regions semiautomatically, through the mediation of user feedback. Geographic information applications are unique in that their domain is highly dynamic. Such data sets are a singularly appropriate environment in which to illustrate our approach to emergent semantics extraction.
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References
Anderson HL, Bajcsy R, Mintz M. A Modular Feedback System for Image Segmentation. GRASP Laboratory. Philadelphia, Pennsylvania, USA; 1987.
Stehling Renato O, Nascimento Mario A, Falco Alexandre X. An adaptive and efficient clustering-based approach for content-based image retrieval. In: Proceedings of the International Database Engineering and Applications Symposium, Grenoble, France; 2001, pp. 356–365.
Gudivada V, Raghavan V. Design and evaluation of algorithms for image retrieval by spatial similarity. ACM Transactions on Information Systems, 1995;13(1):115–144.
Gosselin P, Cord M. Semantic kernel updating for content-based image retrieval. In: Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering, Miami, FL, 2004, pp. 537–544.
Chang E, Li B, GWu, Goh K. Statistical learning for effective visual information retrieval. In: Proceedings of the IEEE International Conference on Image Processing, Barcelona, Spain; 2003, pp. 609–612.
Fournier J, Cord M. A flexible search-by-similarity algorithm for content-based image retrieval. In: Proceedings of the International Conference on Computer Vision, Pattern Recognition and Image Processing, Duram, North CA, 2002, pp. 672–675.
Han J, Li M, Zhang H, Guo L. A memorization learning model for image retrieval. In: Proceedings of the IEEE International Conference on Image Processing, Barcelona, Spain; 2003, pp. 605–608.
Smeulders A, Worring M, Santini S, Gupta A, Jain R. Content-based image retrieval at the end of the early years. Proceedings of the IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000;22(12):1349–1380.
Vasconcelos N, Lippman A. A probabilistic architecture for content-based image retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head Island, SC;2000, pp. 1216–1221.
Xu Y, Olman V, Uberbacher E.Asegmentation algorithm for noisy images. In: Proceedings of the IEEE International Joint Symposia on Intelligence and Systems, Rockville, MD, 1996, pp. 220–226.
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Li, X., Grosky, W., Patel, N., Fotouhi, F. (2007). Extracting Semantics Through Dynamic Context. In: Petrushin, V.A., Khan, L. (eds) Multimedia Data Mining and Knowledge Discovery. Springer, London. https://doi.org/10.1007/978-1-84628-799-2_15
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DOI: https://doi.org/10.1007/978-1-84628-799-2_15
Publisher Name: Springer, London
Print ISBN: 978-1-84628-436-6
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