OpenCV
3.1.0
Open Source Computer Vision
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In this tutorial you will learn how to:
\[d(H_1,H_2) = \frac{\sum_I (H_1(I) - \bar{H_1}) (H_2(I) - \bar{H_2})}{\sqrt{\sum_I(H_1(I) - \bar{H_1})^2 \sum_I(H_2(I) - \bar{H_2})^2}}\]
where\[\bar{H_k} = \frac{1}{N} \sum _J H_k(J)\]
and \(N\) is the total number of histogram bins.\[d(H_1,H_2) = \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)}\]
\[d(H_1,H_2) = \sum _I \min (H_1(I), H_2(I))\]
\[d(H_1,H_2) = \sqrt{1 - \frac{1}{\sqrt{\bar{H_1} \bar{H_2} N^2}} \sum_I \sqrt{H_1(I) \cdot H_2(I)}}\]
*Method* | Base - Base | Base - Half | Base - Test 1 | Base - Test 2 |
---|---|---|---|---|
*Correlation* | 1.000000 | 0.930766 | 0.182073 | 0.120447 |
*Chi-square* | 0.000000 | 4.940466 | 21.184536 | 49.273437 |
*Intersection* | 24.391548 | 14.959809 | 3.889029 | 5.775088 |
*Bhattacharyya* | 0.000000 | 0.222609 | 0.646576 | 0.801869 |