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

by Juan M. Banda, Rafal A. Angryk, Petrus C. Martens
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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, Petrus C. Martens . ImageFARMER: Introducing a Data Mining Framework for the Creation of Large-scale Content-based Image Retrieval Systems. International Journal of Computer Applications. 79, 13 ( October 2013), 8-13. DOI=10.5120/13799-1777

@article{ 10.5120/13799-1777,
author = { Juan M. Banda, Rafal A. Angryk, Petrus C. Martens },
title = { ImageFARMER: Introducing a Data Mining Framework for the Creation of Large-scale Content-based Image Retrieval Systems },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 79 },
number = { 13 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 8-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume79/number13/13799-1777/ },
doi = { 10.5120/13799-1777 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:52:52.768287+05:30
%A Juan M. Banda
%A Rafal A. Angryk
%A Petrus C. Martens
%T ImageFARMER: Introducing a Data Mining Framework for the Creation of Large-scale Content-based Image Retrieval Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 79
%N 13
%P 8-13
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
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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Index Terms

Computer Science
Information Sciences

Keywords

Content-based image retrieval retrieval attribute evaluation dimensionality reduction.

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