Question

我正在寻找一些现有的功能/工具来计算图像中多个ROI（感兴趣区域）的标准Bag of Visual Words直方图。让我解释一下：

（1）假设你有一个图像，其中每个“像素”都带有一个整数：1 ... K 每个这样的“像素”具有以下信息

x，y坐标
从1到K的值

（2）假设大量固定大小的区域是来自所有图像的样本：

（x1，y1） - 顶部，左侧坐标
（x2，y2） - 底部，右坐标

（3）对于每个区域：计算K bin直方图，该直方图计算属于该区域的“像素”值的出现次数

我在MATLAB中实现了以下函数，但由于代码中存在多个for循环，因此非常慢

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 );

我看过OpenCV提出的例程，甚至是英特尔的MKL，但没有找到合适的例子。使用Matlab的分析器，我发现在roi_feat_ids（）中花费了相当多的时间，并且函数sph_roi（）中每个区域的外部循环也很慢。在尝试实现MEX文件之前，我想看看是否可以回收一些现有代码。

Answer 1

我会采取一些措施来加快速度。

应删除最后一行（ids = find( ids );。逻辑掩码比使用find快得多，并且几乎在每种情况下都可以使用find语句。我怀疑这会大大加快你的功能，不损失功能/可读性。
如果你结合了一些s1，s2，s3和s4语句，它可能会更快。
除非需要，否则尽量不要在for循环中创建大型数据集。具体来说，我会删除两行来执行以下操作：ids = roi_feat_ids( scw( j, : ), feat );

后两者可能会节省一些时间，但首先应该节省大量时间。祝你好运！

从图像ROI进行高效的直方图计算

1 个答案: