Understanding HoG output

[Emmanuelle Gouillart]

Hi Brian,

thanks a lot for the explanations! I must say that when I had a look at
the HoG function following Michael's post, I had quite a hard time
understanding how this function was working. Your explanations do help;
do you think it would be possible to write a meaningful example using
HoGs for the example gallery? That might give a good starting point in
order to use this function. If you don't have the time to write the
example yourself, any hints on the possible contents would also help.

Cheers,
Emmanuelle

On Fri, Feb 10, 2012 at 01:45:12PM +0000, Brian Holt wrote:
>    Hi Michael,�

>      Hi, new to group, new to image processing starting to explore HoG.
>      Looking for a python implementation and discovered
>      skimage.feature.hog().

>    Glad to hear someone's using HoGs!

>      My plan to run skimage.features.hog over some positive/negative images
>      and use this to train a svm classifier from scikits-learn. � I am
>      trying to understand the output hog before I proceed further. � �When
>      I run skimag.feature.hog() it over a region of interest �it appears to
>      returns an array. �How do I interpret this array?�

>    My best suggestion would be that you take a look at the comments written
>    in `hog.py`. �Each part quotes the relevant section from the Dalal Triggs
>    paper and then under that implements the code required to do the job.
>    �Others on this list may have different views, but I'd be reluctant to try
>    to 'interpret' the flattened descriptor. �

>      �Is there a way to�reshape the array to see what it was like before it
>      was flattened or�that doesn't make any sense? �Can I plot the descriptor
>      returned in
>      any meaningful way?

>    Yes, you can reshape the array (but that can be a bit tricky), or you
>    could modify the hog function to return the unraveled descriptor and ravel
>    it yourself later if you need it.

>      Also when I choose to visualise the HoG often where I expected to see
>      vertical line dominate, say on the edge of builds, the line drawn
>      often appears to be more dominant at the 45 deg. �Is this expected as
>      the line drawn is really just the sum of all surrounding orientations
>      for the "cell"?

>    A line is drawn for each gradient bin with an intensity proportional to
>    the magnitude of that gradient. �So, the 'star' shape you see for each
>    cell is just the superimposition of all of these lines. You should expect
>    to see dominant lines perpendicular to lines in the image (parallel to the
>    gradient). Also remember that the default is to use 9 bins, so it may be
>    that the 45degree dominant line you see is the closest approximation to
>    horizontal. �You can test this out by trying 8 bins instead of 9.�

>    I hope it helps, feel free to ask any more questions.
>    Regards
>    Brian

>    On 9 February 2012 06:37, bricklemacho <[1]bricklemacho at gmail.com> wrote:

>      Thanks in advance,

>      Michael.