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∞), 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.