Find dominant color on an image

Question

I want to find dominant color on an image. For this, I know that I should use image histogram. But I am not sure of image format. Which one of rgb, hsv or gray image, should be used?

After the histogram is calculated, I should find max value on histogram. For this, should I find below maximum binVal value for hsv image? Why my result image contains only black color?

float binVal = hist.at<float>(h, s);

EDIT :

I have tried the below code. I draw h-s histogram. And my result images are here. I don't find anything after binary threshold. Maybe I find max histogram value incorrectly.

cvtColor(src, hsv, CV_BGR2HSV);

// Quantize the hue to 30 levels
// and the saturation to 32 levels
int hbins = 20, sbins = 22;
int histSize[] = {hbins, sbins};
// hue varies from 0 to 179, see cvtColor
float hranges[] = { 0, 180 };
// saturation varies from 0 (black-gray-white) to
// 255 (pure spectrum color)
float sranges[] = { 0, 256 };
const float* ranges[] = { hranges, sranges };
MatND hist;
// we compute the histogram from the 0-th and 1-st channels
int channels[] = {0, 1};

calcHist( &hsv, 1, channels, Mat(), // do not use mask
         hist, 2, histSize, ranges,
         true, // the histogram is uniform
         false );
double maxVal=0;
minMaxLoc(hist, 0, &maxVal, 0, 0);

int scale = 10;
Mat histImg = Mat::zeros(sbins*scale, hbins*10, CV_8UC3);
int maxIntensity = -100;
for( int h = 0; h < hbins; h++ ) {
    for( int s = 0; s < sbins; s++ )
    {
        float binVal = hist.at<float>(h, s);
        int intensity = cvRound(binVal*255/maxVal);
        rectangle( histImg, Point(h*scale, s*scale),
                    Point( (h+1)*scale - 1, (s+1)*scale - 1),
                    Scalar::all(intensity),
                    CV_FILLED );
        if(intensity > maxIntensity)
            maxIntensity = intensity;
    }
}
std::cout << "max Intensity " << maxVal << std::endl;
Mat dst;
cv::threshold(src, dst, maxIntensity, 255, cv::THRESH_BINARY);

namedWindow( "Dest", 1 );
imshow( "Dest", dst );
namedWindow( "Source", 1 );
imshow( "Source", src );

namedWindow( "H-S Histogram", 1 );
imshow( "H-S Histogram", histImg );

Answer 1

Alternatively you could try a k-means approach. Calculate k clusters with k ~ 2..5 and take the centroid of the biggest group as your dominant color.

The python docu of OpenCv has an illustrated example that gets the dominant color(s) pretty well:

Answer 2

The solution

• Find H-S histogram
• Find peak H value(using minmaxLoc function)
• Split image 3 channel(h,s,v)
• Apply to threshold.
• Create image by merge 3 channel