Abstract
In the past decade, the Internet has rapidly become a most prevalent platform for information sharing and data communications all over the world. This trend has been enhanced by the wide proliferation of home-used digital photos and videos. Although the Web has seen numerous applications for photo storage, sharing, and searching, few image retrieval systems provide satisfactory search service. The so-called semantic gap and curse of dimensionality are two major barriers that existing technologies cannot adequately address. In this paper, we propose a framework to achieve effective and efficient content-base retrieval on images by addressing the above two issues. That is, we need to narrow the semantic gap while mitigating the curse of dimensionality.
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Li, X., Shou, L., Chen, G., Tan, KL. (2007). An Image-Semantic Ontological Framework for Large Image Databases. In: Kotagiri, R., Krishna, P.R., Mohania, M., Nantajeewarawat, E. (eds) Advances in Databases: Concepts, Systems and Applications. DASFAA 2007. Lecture Notes in Computer Science, vol 4443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71703-4_102
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DOI: https://doi.org/10.1007/978-3-540-71703-4_102
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71702-7
Online ISBN: 978-3-540-71703-4
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