如何计算“平均精度和排名”用于CBIR系统 [英] How to calculate "Average Precision and Ranking" for CBIR system

问题描述

所以，因为我已经实现基本的cbir系统使用RGB直方图。现在，我试图生成平均精度和排名曲线。我需要知道，我的公式avg精度是否正确？以及如何计算平均排名？

So, for I have implemented basic cbir system using RGB histograms. Now, I am trying to generate average precision and ranking curves. I need to know that, Is my formula for avg precision correct? and how to calculate average rankings?

Code:
% Dir: parent directory location for images folder c1, c2, c3
% inputImage: \c1\1.ppm
% For example to get P-R curve execute: CBIR('D:\visionImages','\c2\1.ppm');
function [  ] = demoCBIR( Dir,inputImage)
% Dir='D:\visionImages';
% inputImage='\c3\1.ppm';
tic;
S=strcat(Dir,inputImage);
Inp1=imread(S);
num_red_bins = 8;
num_green_bins = 8;
num_blue_bins = 8;
num_bins = num_red_bins*num_green_bins*num_blue_bins;

A = imcolourhist(Inp1, num_red_bins, num_green_bins, num_blue_bins);%input image histogram
srcFiles = dir(strcat(Dir,'\*.jpg'));  
B = zeros(num_bins, 100); % hisogram of other 100 images in category 1
ptr=1;
for i = 1 : length(srcFiles)
    filename = strcat(Dir,'\',srcFiles(i).name);
    I = imread(filename);% filter image
    B(:,ptr) = imcolourhist(I, num_red_bins, num_green_bins, num_blue_bins); 
    ptr=ptr+1;                                                   
end

%normal histogram intersection
a = size(A,2); b = size(B,2); 
K = zeros(a, b);
for i = 1:a
  Va = repmat(A(:,i),1,b);
  K(i,:) = 0.5*sum(Va + B - abs(Va - B));
end


  sims=K;
  for i=1: 100 % number of relevant images for dir 1
     relevant_IDs(i) = i;
  end

 num_relevant_images = numel(relevant_IDs);

 [sorted_sims, locs] = sort(sims, 'descend');
 locations_final = arrayfun(@(x) find(locs == x, 1), relevant_IDs);
 locations_sorted = sort(locations_final);
 precision = (1:num_relevant_images) ./ locations_sorted;
 recall = (1:num_relevant_images) / num_relevant_images;
 % generate Avg precision
 avgprec=sum(precision)/num_relevant_images;% avg precision formula
 plot(avgprec, 'b.-');
 xlabel('Category ID');
 ylabel('Average Precision');
 title('Average Precision Plot');
 axis([0 10 0 1.05]);
end 

推荐答案

您只需将所有精度值相加并求平均。这是平均精度的定义。

Yup that's correct. You simply add up all of your precision values and average them. This is the very definition of average precision.

平均精度只是一个单一数字（通常以百分比），它为您提供图像检索系统的整体性能。值越高，性能越好。 Precision-Recall图表提供了有关系统性能的更详细的细节，但是当您将许多图像检索系统进行比较时，平均精度非常有用。不是绘制许多PR图来尝试和比较许多检索系统的整体性能，您只需要一个表，将所有系统与指定每个系统性能的单个数字（即平均精度）进行比较。

Average precision is simply a single number (usually in percentage) that gives you the overall performance of an image retrieval system. The higher the value, the better the performance. Precision-Recall graphs give you more granular detail on how the system is performing, but average precision is useful when you are comparing a lot of image retrieval systems together. Instead of plotting many PR graphs to try and compare the overall performance of many retrieval systems, you can just have a table that compares all of the systems together with a single number that specifies the performance of each - namely, the average precision.

此外，绘制平均精度没有任何意义。当平均精度通常在科学论文中报告，没有情节......只是一个值！我可以看到你绘制这个的唯一方法是如果你有一个条形图，其中 y -axis表示平均精度，而 x -axis表示要比较的检索系统。条越高，精度越好。然而，一个表格显示了所有不同的检索系统，每个具有它们的平均精度是更合适的。这是在大多数CBIR研究论文中通常做的。

Also, it doesn't make any sense to plot the average precision. When average precision is normally reported in scientific papers, there is no plot.... just a single value! The only way I could see you plotting this is if you had a bar graph, where the y-axis denotes the average precision while the x-axis denotes which retrieval system you are comparing. The higher the bar, the better the accuracy. However, a table showing all of the different retrieval systems, each with their average precision is more than suitable. This is what is customarily done in most CBIR research papers.

为了解决你的另一个问题，使用平均精度。计算您测试的所有检索系统的平均精度，然后根据此平均精度对它们进行排序。具有较高平均精度的系统将排名较高。

To address your other question, you calculate the average rank by using the average precision. Calculate the average precision for all of your retrieval systems you are testing, then sort them based on this average precision. Systems that have higher average precision will be ranked higher.

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