Statistical Features for Image Retrieval - A Quantitative Comparison

Cecilia Di Ruberto, Giuseppe Fodde

Abstract

In this paper we present a comparison between various statistical descriptors and analyze their goodness in classifying textural images. The chosen statistical descriptors have been proposed by Tamura, Battiato and Haralick. In this work we also test a combination of the three descriptors for texture analysis. The databases used in our study are the well-known Brodatz’s album and DDSM(Heath et al., 1998). The computed features are classified using the Naive Bayes, the RBF, the KNN, the Random Forest and Random Tree models. The results obtained from this study show that we can achieve a high classification accuracy if the descriptors are used all together.

References

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Paper Citation


in Harvard Style

Di Ruberto C. and Fodde G. (2014). Statistical Features for Image Retrieval - A Quantitative Comparison . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 610-617. DOI: 10.5220/0004741006100617


in Bibtex Style

@conference{visapp14,
author={Cecilia Di Ruberto and Giuseppe Fodde},
title={Statistical Features for Image Retrieval - A Quantitative Comparison},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={610-617},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004741006100617},
isbn={978-989-758-003-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - Statistical Features for Image Retrieval - A Quantitative Comparison
SN - 978-989-758-003-1
AU - Di Ruberto C.
AU - Fodde G.
PY - 2014
SP - 610
EP - 617
DO - 10.5220/0004741006100617