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If you care about building large-scale object recognition systems, you have to care about speed.  And every little bit of performance counts -- so why not first optimize the stuff which is slowing you down?

NMS (non-maximum suppression) is a very popular post-processing method for eliminating redundant object detection windows.  I have take Felzenszwalb et al.'s nms.m and made it significantly faster by eliminating an inner loop.  6 years of grad school, 6 years of building large-scale vision systems in Matlab, and you really learn how to vectorize code.  The code I call millions of times needs to be fast, and nms is one of those routines I call all the time.

The code is found below as a Github gist -- which was taken from my Exemplar-SVM object recognition library (from my ICCV2011 paper: Ensemble of Exemplar-SVMs for Object Detection and Beyond).  The same file, nms.m, can also be found a part of the Exemplar-SVM library on Github.  In fact, this code produces the same result as Pedro's code, but is much faster.  Here is one timing experiment I performed when performing nms on ~300K windows, where my version is roughly 100 times faster.  When you deal with exemplar-SVMs you have to deal with lots of detectors (i.e., lots of detection windows), so fast NMS is money.


>> tic;top = nms_original(bbs,.5);toc
Elapsed time is 58.172313 seconds.


>> tic;top = nms_fast(bbs,.5);toc
Elapsed time is 0.532638 seconds.

	function top = nms(boxes, overlap)
	% top = nms_fast(boxes, overlap)
	% Non-maximum suppression. (FAST VERSION)
	% Greedily select high-scoring detections and skip detections
	% that are significantly covered by a previously selected
	% detection.
	% NOTE: This is adapted from Pedro Felzenszwalb's version (nms.m),
	% but an inner loop has been eliminated to significantly speed it
	% up in the case of a large number of boxes
	% Tomasz Malisiewicz (tomasz@cmu.edu)
	if isempty(boxes)
	top = [];
	return;
	end
	x1 = boxes(:,1);
	y1 = boxes(:,2);
	x2 = boxes(:,3);
	y2 = boxes(:,4);
	s = boxes(:,end);
	area = (x2-x1+1) .* (y2-y1+1);
	[vals, I] = sort(s);
	pick = s*0;
	counter = 1;
	while ~isempty(I)
	last = length(I);
	i = I(last);
	pick(counter) = i;
	counter = counter + 1;
	xx1 = max(x1(i), x1(I(1:last-1)));
	yy1 = max(y1(i), y1(I(1:last-1)));
	xx2 = min(x2(i), x2(I(1:last-1)));
	yy2 = min(y2(i), y2(I(1:last-1)));
	w = max(0.0, xx2-xx1+1);
	h = max(0.0, yy2-yy1+1);
	o = w.*h ./ area(I(1:last-1));
	I([last; find(o>overlap)]) = [];
	end
	pick = pick(1:(counter-1));
	top = boxes(pick,:);

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