Abstract
While the searching of text document has grown relatively mature on the Internet, the searching of images and other forms of multimedia data significantly lags behind. To search visual information on the basis of semantic concepts requires both their discovery and meaningful indexing. By analyzing the users’ search, relevance feedback and selection patterns, we propose a method which allows semantic concepts to be discovered and migrated through an index hierarchy. Our method also includes a robust scoring mechanism that permits faulty indexing to be rectified over time. These include: (i) repeated and sustained corroboration of specific index terms before installation, and (ii) the ability for the index score to be both incremented and decremented. Experimental results indicate that convergence to an optimum index level may be achieved in reasonable time periods through such dynamic index evolution.
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Leung, C., Liu, J. (2007). Multimedia Data Mining and Searching Through Dynamic Index Evolution. In: Qiu, G., Leung, C., Xue, X., Laurini, R. (eds) Advances in Visual Information Systems. VISUAL 2007. Lecture Notes in Computer Science, vol 4781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76414-4_30
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DOI: https://doi.org/10.1007/978-3-540-76414-4_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76413-7
Online ISBN: 978-3-540-76414-4
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