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How to Cope with the Performance Gap in Content-Based Image Retrieval Systems

How to Cope with the Performance Gap in Content-Based Image Retrieval Systems

Agma J.M. Traina, Caetano Traina Jr., Cristina D.A. Ciferri, Marcela X. Ribeiro, Paulo M. Azevedo-Marques
Copyright: © 2009 |Volume: 4 |Issue: 1 |Pages: 21
ISSN: 1555-3396|EISSN: 1555-340X|ISSN: 1555-3396|EISBN13: 9781615202904|EISSN: 1555-340X|DOI: 10.4018/jhisi.2009010104
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MLA

Traina, Agma J.M., et al. "How to Cope with the Performance Gap in Content-Based Image Retrieval Systems." IJHISI vol.4, no.1 2009: pp.47-67. http://doi.org/10.4018/jhisi.2009010104

APA

Traina, A. J., Traina Jr., C., Ciferri, C. D., Ribeiro, M. X., & Azevedo-Marques, P. M. (2009). How to Cope with the Performance Gap in Content-Based Image Retrieval Systems. International Journal of Healthcare Information Systems and Informatics (IJHISI), 4(1), 47-67. http://doi.org/10.4018/jhisi.2009010104

Chicago

Traina, Agma J.M., et al. "How to Cope with the Performance Gap in Content-Based Image Retrieval Systems," International Journal of Healthcare Information Systems and Informatics (IJHISI) 4, no.1: 47-67. http://doi.org/10.4018/jhisi.2009010104

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Abstract

This paper discusses the main aspects regarding the performance gap in Content-based Image Retrieval (CBIR) systems, which is an important issue regarding their acceptability. We also detail the main problems that lead to the performance gap: the use of many features to represent images, the lack of appropriate indexing structures for images and features, deficient query plans employed to execute similarity queries, and sometimes the poor quality of results obtained by the CBIR system. We present guidelines to overcome these problems by employing feature selection techniques to beat the "dimensionality curse", by using proper access methods to support fast and effective indexing and retrieval of images, by stressing the importance of using query optimization approaches and by including the user during the tuning of the CBIR system through relevance feedback techniques.

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