Hello, for nowadays, I am still working on the HOG features. In this article,lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf, it said that, for RGB images, it choose the biggest value of the gradient in each channel to be the gradient of the RGB images, could someone give me a reason why they did like this? Thank you very much.
because the biggest value would be the prominent value representing the feature at specific pixel and then a group of pixels or block
so if you want to extract features at a group of pixels and you calculate the gradient at these pixels in the three RGB channels, and then it's more logic to pick the channel with higher gradient magnitude value as it's expected to be more efficient to represent features in this location.
Well, its clearly written in section "6.2 Gradient Computation": "For colour images, we calculate separate gradients for each colour channel, and take the one with the largest norm as the pixel's gradient vector." The task is to find a strong measure for the computation of the orientation of an object in an image or the image content. Therefore, the authors "measure the distance" (the largest norm) and apply it / utilize it as a gradient vector. Why? Usually one RGB-channel performs best regarding the contrast in an colour image. This is why the search for the largest norm. That's it.
Might be interesting to see that most applications tend to ignore the color information, skip directly to a grayscale form of the input and thus keep only one possible channel to calculate gradient features. Especially when changing lighting conditions are present in your application you should avoid selecting color channel specific features when using a rigid model based on gradients.
Biggest value indicates major gray level difference at that location.
Which is indication of boundary or any information related to object.
That's why it is selected.
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