Non-maximum suppression(非极大值抑制算法）

在RCNN系列目标检测中，有一个重要的算法，用于消除一些冗余的bounding box，这就是non-maximum suppression算法。

这里有一篇博客写的挺好的：

　　http://www.cnblogs.com/liekkas0626/p/5219244.html

借用博客里的两张图，如有问题，请联系我删除。

在目标检测中，这些bounding box都表示检测到了人脸，并且会给每一个bounding box一个score，最终我们需要只保留score最大的bounding box（记为bounding box1），將与bounding box1 overlap (重叠) 较大的一些bounding box消除，即只保留在这个局部区域score最高的一个bounding box。可能在一张图中有多个人脸，那么我们就需要保留各个局部区域score最大的bounding box，两个bounding box的重叠程度一般用IOU的值作为衡量，

IOU = 两个bounding box的围成的区域的交集 / 两个bounding box围成区域的并集。

 

function pick = nms(boxes, overlap)
% top = nms(boxes, overlap)
% Non-maximum suppression. (FAST VERSION)
% Greedily select high-scoring detections and skip detections
% that are significantly covered by a previously selected
% detection.
%################################################################
% input   -----    boxes: object detection window
%                         size(boxes) = (n,5)
%                         n --- the number of window
%                       (xMin, yMin, xMax, yMax, score)
%                  overlap: suppression threshold
%
% output  -----    pick:the index of reserved window
if isempty(boxes)
  pick = [];
  return;
end

x1 = boxes(:,1);
y1 = boxes(:,2);
x2 = boxes(:,3);
y2 = boxes(:,4);
s = boxes(:,end);

% calculate the area of all detections
area = (x2-x1+1) .* (y2-y1+1);
% vals is the sorted elments in ascending order, I is the corresponding index
[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);
  
  inter = w.*h;
  o = inter ./ (area(i) + area(I(1:last-1)) - inter);
  
  I = I(find(o<=overlap));
end

pick = pick(1:(counter-1));