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On Hierarchical Content-Based Image Retrieval by Dynamic Indexing and Guided Search

On Hierarchical Content-Based Image Retrieval by Dynamic Indexing and Guided Search

Jane You, Qin Li, Jinghua Wang
Copyright: © 2010 |Volume: 4 |Issue: 4 |Pages: 19
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781613502433|DOI: 10.4018/jcini.2010100102
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MLA

You, Jane, et al. "On Hierarchical Content-Based Image Retrieval by Dynamic Indexing and Guided Search." IJCINI vol.4, no.4 2010: pp.18-36. http://doi.org/10.4018/jcini.2010100102

APA

You, J., Li, Q., & Wang, J. (2010). On Hierarchical Content-Based Image Retrieval by Dynamic Indexing and Guided Search. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 4(4), 18-36. http://doi.org/10.4018/jcini.2010100102

Chicago

You, Jane, Qin Li, and Jinghua Wang. "On Hierarchical Content-Based Image Retrieval by Dynamic Indexing and Guided Search," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 4, no.4: 18-36. http://doi.org/10.4018/jcini.2010100102

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Abstract

This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending data mining and data warehousing techniques. The proposed algorithms include a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing. It also provides an image data schema for hierarchical image representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features. Experimental results confirm that the new approach is feasible for content-based image retrieval.

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