Efficient matching of large-size histograms

As we know, histogram matching is a commonly-adopted technique in the applications of pattern recognition. The matching of two patterns can be accomplished by matching their corresponding histograms. In general, the number of features and the resolution of each feature will determine the size of histogram. The more the number of features and the higher the resolution of each feature, the stronger the discrimination capability of histogram will be. Unfortunately, the increase of histogram size will lead to the decrease of the efficiency of histogram matching because traditional algorithms in evaluating similarity are all relevant to the histogram size. In this paper, a novel histogram-matching algorithm is proposed whose efficiency is irrelevant to the histogram size. The proposed algorithm can be applied to commonly-adopted histogram similarity measurement functions, such as histogram intersection function, L1 norm, L2 norm, χ2 test and so on. By adopting our proposed algorithm, future researchers can focus more on the selection and combination of histogram features and freely adjust the resolution of each feature without worrying the decrease of retrieval efficiency.