Compares two histograms

d = cv.compareHist(H1, H2)
d = cv.compareHist(H1, H2, 'OptionName',optionValue, ...)

Input

• H1 First compared histogram. Both dense or sparse arrays are supported (single-precision floating-point).
• H2 Second compared histogram of the same size and type as H1.

Output

• d Output distance d(H1,H2).

Options

• Method Comparison method, default 'Correlation'. One of:
  • Correlation Correlation.
  • ChiSquare Chi-Square.
  • Intersection Intersection.
  • Bhattacharyya Bhattacharyya distance (In fact, OpenCV computes Hellinger distance, which is related to Bhattacharyya coefficient).
  • Hellinger Synonym for 'Bhattacharyya'.
  • AltChiSquare Alternative Chi-Square.
  • KullbackLeibler Kullback-Leibler divergence.

The function cv.compareHist compares two dense or two sparse histograms using the specified method.

While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms or more general sparse configurations of weighted points, consider using the cv.EMD function.

Histogram comparison methods:

• Correlation

  d(H1,H2) = (sum_{I}((H1(I) - H1_hat(I))*(H2(I) - H2_hat(I)))) /
             (sum_{I}(H1(I) - H1_hat(I))^2 * sum_{I}(H2(I) - H2_hat(I))^2)
  

  where Hk_hat = 1/N * sum_{J} Hk(J) and N is a total number of histogram bins.

• ChiSquare

  d(H1,H2) = sum_{I} ((H1(I) - H2(I))^2 / H1(I))
  

• Intersection

  d(H1,H2) = sum_{I} min(H1(I), H2(I))
  

• Bhattacharyya

  d(H1,H2) = sqrt(1 - (1/sqrt(H1_hat(I)*H2_hat(I)*N^2)) * sum_{I}(sqrt(H1(I)*H2(I))))
  

• AltChiSquare

  d(H1,H2) = 2 * sum_{I} ((H1(I) - H2(I))^2 / (H1(I) + H2(I)))
  

  This alternative formula is regularly used for texture comparison. See [Puzicha1997].

• KullbackLeibler

  d(H1,H2) = sum_{I} (H1(I) * log(H1(I)/H2(I)))
  

References

[Puzicha1997]:

Jan Puzicha, Thomas Hofmann, and Joachim M Buhmann. "Non-parametric similarity measures for unsupervised texture segmentation and image retrieval". In Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on, pages 267-272.