Efficiently compute Felzenszwalb's HOG (FHOG) features.
A fast implementation of the HOG variant used by Felzenszwalb et al.
in their work on discriminatively trained deformable part models.
http://www.cs.berkeley.edu/~rbg/latent/index.html
Gives nearly identical results to features.cc in code release version 5
but runs 4x faster (over 125 fps on VGA color images).
The computed HOG features are 3*nOrients+5 dimensional. There are
2*nOrients contrast sensitive orientation channels, nOrients contrast
insensitive orientation channels, 4 texture channels and 1 all zeros
channel (used as a 'truncation' feature). Using the standard value of
nOrients=9 gives a 32 dimensional feature vector at each cell. This
variant of HOG, refered to as FHOG, has been shown to achieve superior
performance to the original HOG features. For details please refer to
work by Felzenszwalb et al. (see link above).
This function is essentially a wrapper for calls to gradientMag()
and gradientHist(). Specifically, it is equivalent to the following:
[M,O] = gradientMag( I,0,0,0,1 ); softBin = -1; useHog = 2;
H = gradientHist(M,O,binSize,nOrients,softBin,useHog,clip);
See gradientHist() for more general usage.
This code requires SSE2 to compile and run (most modern Intel and AMD
processors support SSE2). Please see: http://en.wikipedia.org/wiki/SSE2.
USAGE
H = fhog( I, [binSize], [nOrients], [clip], [crop] )
INPUTS
I - [hxw] color or grayscale input image (must have type single)
binSize - [8] spatial bin size
nOrients - [9] number of orientation bins
clip - [.2] value at which to clip histogram bins
crop - [0] if true crop boundaries
OUTPUTS
H - [h/binSize w/binSize nOrients*3+5] computed hog features
EXAMPLE
I=imResample(single(imread('peppers.png'))/255,[480 640]);
tic, for i=1:100, H=fhog(I,8,9); end; disp(100/toc) % >125 fps
figure(1); im(I); V=hogDraw(H,25,1); figure(2); im(V)
EXAMPLE
% comparison to features.cc (requires DPM code release version 5)
I=imResample(single(imread('peppers.png'))/255,[480 640]); Id=double(I);
tic, for i=1:100, H1=features(Id,8); end; disp(100/toc)
tic, for i=1:100, H2=fhog(I,8,9,.2,1); end; disp(100/toc)
figure(1); montage2(H1); figure(2); montage2(H2);
D=abs(H1-H2); mean(D(:))
See also hog, hogDraw, gradientHist
Piotr's Computer Vision Matlab Toolbox Version 3.23
Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
Licensed under the Simplified BSD License [see external/bsd.txt]