OpenCV Q&A Forum - RSS feedhttp://answers.opencv.org/questions/OpenCV answersenCopyright <a href="http://www.opencv.org">OpenCV foundation</a>, 2012-2018.Thu, 09 Jul 2015 20:45:57 -0500Best color difference or distance approximation?http://answers.opencv.org/question/65946/best-color-difference-or-distance-approximation/Currently, a standard way of comparing colors is using "Delta E" metric in CIELab [[Color-difference](https://en.wikipedia.org/wiki/Color_difference)] which is based on Euclidean distance in CIELab color space.
However, for certain applications using the distance metric intensively "Delta E" metric could be a bit slow (e.g. RGB2Lab conversion is necessary, floating point operations can be costly, etc.).
Is there a "good enough approximation" of color difference or distance?
Ex.
* Weighted Manhattan distance (L1 distance) (in RGB) (as suggested [here](http://stackoverflow.com/questions/9018016/how-to-compare-two-colors))
* Hue Manhattan distance (L1 distance) (in HSV) (as suggested [here](http://stackoverflow.com/questions/9018016/how-to-compare-two-colors))
* Any other suggestions?mkcThu, 09 Jul 2015 20:45:57 -0500http://answers.opencv.org/question/65946/Change distance function for kmeans clusteringhttp://answers.opencv.org/question/5880/change-distance-function-for-kmeans-clustering/Hi there,
I have a question concerning the [kmeans][1] method for clustering data-points. By default this method uses the L2 norm (euclidean distance) to cluster the provided data. Is there any possibility for using another distance metric?
[1]:http://opencv.willowgarage.com/documentation/cpp/clustering_and_search_in_multi-dimensional_spaces.html#cv-kmeans(Clustering and Search in Multi-Dimensional Spaces)jstrThu, 10 Jan 2013 07:30:16 -0600http://answers.opencv.org/question/5880/