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ImageFARMER: Introducing a Data Mining Framework for the Creation of Large-scale Content-based Image Retrieval Systems

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International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 79 - Number 13
Year of Publication: 2013
Authors:
Juan M. Banda
Rafal A. Angryk
Petrus C. Martens
10.5120/13799-1777

Juan M Banda, Rafal A Angryk and Petrus C Martens. Article: ImageFARMER: Introducing a Data Mining Framework for the Creation of Large-scale Content-based Image Retrieval Systems. International Journal of Computer Applications 79(13):8-13, October 2013. Full text available. BibTeX

@article{key:article,
	author = {Juan M. Banda and Rafal A. Angryk and Petrus C. Martens},
	title = {Article: ImageFARMER: Introducing a Data Mining Framework for the Creation of Large-scale Content-based Image Retrieval Systems},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {79},
	number = {13},
	pages = {8-13},
	month = {October},
	note = {Full text available}
}

Abstract

In this paper we introduce imageFARMER, a framework that allows information retrieval researchers and educators to develop and customize domain-specific content-based image retrieval systems with ease while developing a deeper understanding of the underlying representation of domain-specific image data. imageFARMER incorporates different aspects of image processing and content-based information retrieval, such as: image representation via image parameter extraction, validation via image parameters, analysis of multiple dissimilarity measures for accurate data analysis, testing of dimensionality reduction methods for storage and processing optimization, and indexing algorithms for fast and efficient querying. The unique capabilities of this framework have not been available together as an open-source software package designed for research, while offering enhanced knowledge discovery and validation of all steps involved when creating large-scale content-based image retrieval systems.

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