How to visualize the 32 dimension HOG from DPM?

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If you want to create a visualization for HOG you'll need to understand what is it that you are calculating with HOG.

In HOG, you evaluate blocks that are subdivided into cells of pixels. In your case, you have 8x8 pixel cells and 32x32 blocks. So, your block has 4x4 cells. Look at the image bellow to better understand what I am talking about.

So, HOG is all about evaluating edges of images. Generically, a local HOG feature is computed inside one cell and has two components, gradient (G), which is a measure of strength of an edge, and orientation (theta), that you refer to as "bin". This bin is a way to simplify the feature. So, instead of each feature being able to assume any angle, it can only assume 18 (in your case) bins. Each bin interval is of 20 degrees (360 / 18) so, if the angle is, for example, 30, this means it fell into bin 2.

   //-- pseudo-code
    if (0 < angle < 20)
       bin = 1;
    else if (20 < angle < 40)
       bin = 2;
    ....

(Ix and Iy are the intensity of the pixel in the horizontal and vertical gradient images)

So, in your case each block will be described by 16 (4x4 cells) features, each of these features is represented by a gradient and an orientation. To visualize this, just draw lines in the centre of each block, the line growing with gradient, and the orientation of the line is set by the bin, so that there can only be 18 different orientations. See image bellow for an example.

Summing up, for each block you will draw 16 lines with different sizes and orientations depending to the gradient and orientation. Keep in mind that the gradients need to be normalized to fit in the blocks.

Hope this helps, good luck.

ADDED BY @StevenPuttemans

To complete this nice explanation of Pedro, I have written code to visualize HOG features exactly like this, based on some earlier work of Jurgen Brauer, which is no longer foundable on the net ...

// HOGDescriptor visual_image analyzing
// Adapted from http://www.juergenwiki.de/work/wiki/doku.php?id=public%3ahog_descriptor_computation_and_visualization
// ONLY PRECAUSIONS ARE
// --> Image size width/heigth needs to be a multiple of block width/heigth
// --> Block size width/heigth (multiple cells) needs to be a multiple of cell size (histogram region) width/heigth
// --> Block stride needs to be a multiple of a cell size, however current code only allows to use a block stride = cell size!
// --> ScaleFactor enlarges the image patch to make it visible (e.g. a patch of 50x50 could have a factor 10 to be visible at scale 500x500 for inspection)
// --> viz_factor enlarges the maximum size of the maximal gradient length for normalization. At viz_factor = 1 it results in a length = half the cell width
Mat get_hogdescriptor_visual_image(Mat& origImg, vector<float>& descriptorValues, Size winSize, Size cellSize, int scaleFactor, double viz_factor)
{
    Mat visual_image;
    resize(origImg, visual_image, Size(origImg.cols*scaleFactor, origImg.rows*scaleFactor));

    int gradientBinSize = 9;
    float radRangeForOneBin = 3.14/(float)gradientBinSize; // dividing ...