Comparison of similarity metrics for texture image retrieval

Similarity metrics plays an important role in content-based image retrieval. The paper compares nine image similarity measures - Manhattan (L1), weighted-mean-variance (WMV), Euclidean (L2), Chebychev (L/spl infin/), Mahalanobis, Canberra, Bray-Curtis, squared chord and squared chi-squared distances...

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Published inTENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region Vol. 2; pp. 571 - 575 Vol.2
Main Authors Kokare, M., Chatterji, B.N., Biswas, P.K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2003
Subjects
Content based retrieval
Discrete wavelet transforms
Euclidean distance
Image databases
Image retrieval
Information retrieval
Shape
Software libraries
Spatial databases
Video sharing
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Summary:Similarity metrics plays an important role in content-based image retrieval. The paper compares nine image similarity measures - Manhattan (L1), weighted-mean-variance (WMV), Euclidean (L2), Chebychev (L/spl infin/), Mahalanobis, Canberra, Bray-Curtis, squared chord and squared chi-squared distances - for texture image retrieval. A large texture database of 1856 images, derived from the Brodatz album, is used to check the retrieval performance. Features of all the database images were extracted using the Gabor wavelet. Experimental results on the Brodatz texture database indicate that the retrieval performance can be improved significantly by using the Canberra and Bray-Curtis distance metrics as compare to traditional Euclidean and Mahalanobis distance based approaches.
ISBN:0780381629
9780780381629
DOI:10.1109/TENCON.2003.1273228