A Fast Distance Between Histograms
Part of the
Lecture Notes in Computer Science
book series (LNCS, volume 3773)
In this paper we present a new method for comparing histograms. Its main advantage is that it takes less time than previous methods.
The present distances between histograms are defined on a structure called signature, which is a lossless representation of histograms. Moreover, the type of the elements of the sets that the histograms represent are ordinal, nominal and modulo.
We show that the computational cost of these distances is O(z′) for the ordinal and nominal types and O(z ′2) for the modulo one, where z′ is the number of non-empty bins of the histograms. In the literature, the computational cost of the algorithms presented depends on the number of bins in the histograms. In most applications, the histograms are sparse, so considering only the non-empty bins dramatically reduces the time needed for comparison.
The distances we present in this paper are experimentally validated on image retrieval and the positioning of mobile robots through image recognition.
KeywordsImage Retrieval Pattern Recognition Letter Operation Move Move Left Histogram Representation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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