CODEWITHSUNDEEP
image-processinggraphicscomputer-vision
TheBoss
TheBoss

Reputation: 2044

Image retrieval - edge histogram

My lecturer has slides on edge histograms for image retrieval, whereby he states that one must first divide the image into 4x4 blocks, and then check for edges at the horizontal, vertical, +45°, and -45° orientations. He then states that this is then represented in a 14x1 histogram. I have no idea how he came about deciding that a 14x1 histogram must be created. Does anyone know how he came up with this value, or how to create an edge histogram?

Thanks.

Upvotes: 1

Views: 1409

Answers (1)

ely
ely

Reputation: 77424

The thing you are referring to is called the Histogram of Oriented Gradients (HoG). However, the math doesn't work out for your example. Normally you will choose spatial binning parameters (the 4x4 blocks). For each block, you'll compute the gradient magnitude at some number of different directions (in your case, just 2 directions). So, in each block you'll have N_{directions} measurements. Multiply this by the number of blocks (16 for you), and you see that you have 16*N_{directions} total measurements.

To form the histogram, you simply concatenate these measurements into one long vector. Any way to do the concatenation is fine as long as you keep track of the way you map the bin/direction combo into a slot in the 1-D histogram. This long histogram of concatenations is then most often used for machine learning tasks, like training a classifier to recognize some aspect of images based upon the way their gradients are oriented.

But in your case, the professor must be doing something special, because if you have 16 different image blocks (a 4x4 grid of image blocks), then you'd need to compute less than 1 measurement per block to end up with a total of 14 measurements in the overall histogram.

Alternatively, the professor might mean that you take the range of angles in between [-45,+45] and you divide that into 14 different values: -45, -45 + 90/14, -45 + 2*90/14, ... and so on.

If that is what the professor means, then in that case you get 14 orientation bins within a single block. Once everything is concatenated, you'd have one very long 14*16 = 224-component vector describing the whole image overall.

Incidentally, I have done a lot of testing with Python implementations of Histogram of Gradient, so you can see some of the work linked here or here. There is also some example code at that site, though a more well-supported version of HoG appears in scikits.image.

Upvotes: 3

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