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A Generic Scheme for Color Image Retrieval Based on the Multivariate Wald-Wolfowitz Test
June 2005 (vol. 17 no. 6)
pp. 808-819
ASCII Text
x 
 
Christos Theoharatos, Nikolaos A. Laskaris, George Economou, Spiros Fotopoulos, "A Generic Scheme for Color Image Retrieval Based on the Multivariate Wald-Wolfowitz Test," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 808-819, June, 2005.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2005.85,
author = {Christos Theoharatos and Nikolaos A. Laskaris and George Economou and Spiros Fotopoulos},
title = {A Generic Scheme for Color Image Retrieval Based on the Multivariate Wald-Wolfowitz Test},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {17},
number = {6},
issn = {1041-4347},
year = {2005},
pages = {808-819},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2005.85},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - A Generic Scheme for Color Image Retrieval Based on the Multivariate Wald-Wolfowitz Test
IS - 6
SN - 1041-4347
SP808
EP819
EPD - 808-819
A1 - Christos Theoharatos,
A1 - Nikolaos A. Laskaris,
A1 - George Economou,
A1 - Spiros Fotopoulos,
PY - 2005
KW - Image retrieval
KW - multivariate statistics
KW - sampling
KW - graph-theoretic methods
KW - similarity measures
KW - multivariate visualization.
VL - 17
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
In this study, a conceptually simple, yet flexible and extendable strategy to contrast two different color images is introduced. The proposed approach is based on the multivariate Wald-Wolfowitz test, a nonparametric test that assesses the commonality between two different sets of multivariate observations. It provides an aggregate gauge of the match between color images, taking into consideration all the (selected) low-level characteristics, while alleviating correspondence issues. We show that a powerful measure of similarity between two color images can emerge from the statistical comparison of their representations in a properly formed feature space. For the sake of simplicity, the RGB-space is selected as the feature space, while we are experimenting with different ways to represent the images within this space. By altering the feature-extraction implementation, complementary ways to portray the image content appear. The reported results, from the application on a diverse collection of images, clearly demonstrate the effectiveness of our method, its superiority over previous methods, and suggest that even further improvements can be achieved along the same line of research. It is not only the unifying character that makes our strategy appealing, but also the fact that the retrieval performance does not increase continuously with the amount of details in the image representation. The latter sets an upper limit to the computational demands and reminds of performance plateaus reached by novel approaches in information retrieval.

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Index Terms:
Image retrieval, multivariate statistics, sampling, graph-theoretic methods, similarity measures, multivariate visualization.
Citation:
Christos Theoharatos, Nikolaos A. Laskaris, George Economou, Spiros Fotopoulos, "A Generic Scheme for Color Image Retrieval Based on the Multivariate Wald-Wolfowitz Test," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 808-819, June 2005, doi:10.1109/TKDE.2005.85
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