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Tf-idf weighting scheme is adopted by state-ofthe-art object retrieval systems to reflect the difference in discriminability between visual words. However, we argue it is only suboptimal by noting that tf-idf weighting scheme does not take quantization error into account and exploit word correlation. We view tf-idf weights as an example of diagonal Mahalanobis-type similarity matrix and generalize it into a sparse one by selectively activating off-diagonal elements. Our goal is to separatedoi:10.1109/ijcnn.2013.6707063 dblp:conf/ijcnn/YanY13 fatcat:uxfjeirhpva2reedkbi7bdhxca