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There are many good measures to estimate the difference and similarity between two histograms such as log-likelihood ratio and chi-square statistic [1].
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The Bhattacharyya distance between two histograms is defined as (18).
Various metrics have been presented to evaluate the dissimilarity between two histograms.
Similarly, we measured the KL divergence between two histograms of saliency measures: the histogram of saliency measures at the fixated locations in a test scene and the histogram of saliency measures at the same locations in a different scene randomly selected from the dataset [31].
To measure the distance between two histograms we employ the histogram intersection distance metric, defined as (7) d (H i, H j ) = 1 − ∑ k = 1 B min (H i, k, H j, k ), where H i and H j are two normalized histograms, B denotes the number of bins used in our histograms, and H i, k and H j, k represent the value of the kth bin of histogram H i and H j, respectively.
Afterwards, two separate histograms such as H_D C T U P and H_D C T L P of these two binary patterns are calculated using Eqs.
Our technique uses the Kolmogorov-Smirnov test [29] as a metric to evaluate the similarity between the two histograms.
The visual similarity between the two histograms is confirmed by correlation analysis.
We also measured the difference by computing the overlap between the two histograms.
The degree of overlap between the two histograms is indicative for the discriminative accuracy of the risk model.
Thus, the differences between the two histograms primarily reflect the impact of continuous light on gene expression in Cyanothece 51142.
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