Efficient histogram computation from image ROIs - performance

Question

I am looking for some already existing functions/tools to compute standard Bag of Visual Words histograms from multiple ROI (Regions of Interest) in an image. Let me explain:
(1) Suppose you have an image where each "pixel" carries an integer: 1 ... K
Each such "pixel" has following information
x,y coordinate
a value from 1 to K
(2) Suppose a LARGE amount of fixed size regions are sample from all the image in format:
(x1,y1) - top,left coordinate
(x2,y2) - bottom,right coordinate
(3) For every region: Compute a K bin histogram that counts number of occurrences of the "pixel" values that fall in that region
I have implemented a following function in MATLAB but due to multiple for loops in the code, it is very slow
function [H words] = sph_roi( wind, tree, desc, feat, bins )
% FUNCTION computes an SPH histogram for a collection of windows. Spatial
% information is captured by splitting the window in bins horizontally.
%
% [H words] = sph_roi( obj_wind, tree, desc, feat, [ bins ] );
%
% INPUT :
% wind - sampled ROI windows
% [left_x, top_y, right_x, bottom_y] - see sample_roi()
% tree - vocabulary tree
% desc - descriptors matrix
% feat - features matrix
% bins - number of horizontal cells (1=BOVW, 2... SPH)
% by default set to the multiples of window height.
%
% OUTPUT :
% H - SPH histograms
% words - word IDs found for every descriptor
%
verbose = 0;
% input argument number check
if nargin < 4
error( 'At least 4 input arguments required.' );
end
% default number of horizontal cells
if nargin < 5
bins = -1; % will be set in multiples of each window height corresp.
end
% number of windows
num_wind = size( wind, 1 );
% number of visual words
num_words = tree.K;
% pre-compute all visual words
words = vl_hikmeanspush( tree, desc );
% initialize SPH histograms matrix
H = zeros( num_words * bins, num_wind );
% compute BOVW for each ROI
for i = 1 : num_wind
if verbose == 1
fprintf( 'sph_roi(): processing %d / %d\n', i, num_wind );
end
% pick a window
wind_i = wind( i, : );
% get the dimensions of the window
[w h] = wind_size( wind_i );
% if was not set - the number of horizontal bins
if bins == -1
bins = round( w / h );
end
% return a list of subcell windows
scw = create_sph_wind( wind_i, bins );
for j = 1 : bins
% pick a cell
wind_tmp = scw( j, : );
% get the descriptor ids falling in that cell
ids = roi_feat_ids( wind_tmp, feat );
% compute the BOVW histogram for the current cell
h = vl_hikmeanshist( tree, words(ids) );
% assemble the SPH histogram in the output matrix directly
H( 1+(j-1)*num_words : j*num_words, i ) = h( 2:end );
end
end
function ids = roi_feat_ids( w, f )
% FUNCTION returns those feature ids that fall in the window.
%
% ids = roi_feat_ids( w, f );
%
% INPUT :
% w - window
% f - all feature points
%
% OUTPUT :
% ids - feature ids
%
% input argument number check
if nargin ~= 2
error( 'Two input arguments required.' );
end
left_x = 1;
top_y = 2;
right_x = 3;
bottom_y = 4;
% extract and round the interest point coordinates
x = round( f(1,:) );
y = round( f(2,:) );
% bound successively the interest points
s1 = ( x > w(left_x) ); % larger than left_x
s2 = ( x < w(right_x) ); % smaller than right_x
s3 = ( y > w(top_y) ); % larger than top_y
s4 = ( y < w(bottom_y) ); % smaller than bottom_y
% intersection of these 4 sets are the ROI enclosed interest points
ids = s1 & s2 & s3 & s4;
% convert ids to real
ids = find( ids );
I've looked at routines proposed by OpenCV and even in Intel's MKL but found nothing appropriate. Using the Matlab's profiler, I found that considerable time is spent in the roi_feat_ids() and the outer loop over each region in the function sph_roi() is slow too. Before trying to implement a MEX file, I would like to see if I could recycle some existing code.

Answer 1

There's a few things that I would do to speed this up.
The very last line should be removed (ids = find( ids );. Logical masks are much faster than using a find, and they work in almost every case that a find statement would work. I suspect this will speed up your function considerably, at no loss of functionality/ readability.
It might be quicker if you combined some of the s1, s2, s3, and s4 statements.
Try not to create large data sets in the for loop unless they are required. Specifically, I would remove two lines to do the following: ids = roi_feat_ids( scw( j, : ), feat );
The latter two might save you a bit of time, but the first should be a huge time saver. Good luck!