Matching an image with a database

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Shizuka
Shizuka on 16 Mar 2013
Commented: Hamad ALshahrani on 30 Mar 2020
I am looking to compare a new image to a database of images, and then output the higher "similarity". The images I want to compare are similar, but the problem is though because they're not pixel by pixel equal. I've tried to use BoW (Bag Of Words) model already, as per recommendation, I tried various implementations without success, the best correct rate I got was 30%, which is something really low.
Let me show you what I am talking about: (imgur gallery with 5 example images) http://imgur.com/a/ukf5E#0. I want to detect that the four initial images are equal, and the fifth one is different. I wouldn't mind only detecting that the ones with the same angle orientation are equal, though. (In my example 2, 3 and 4)
So, that being said, are there any better methods than BoW for that? Or perhaps BoW should be enough if I implemented in a different way?
Thanks in advance.
  2 Comments
Aravind Dhakshinamoorthy
Aravind Dhakshinamoorthy on 14 Dec 2016
Try using the SIFT algorithm. It detects key points in both images and maps the similar ones. Greater number of similar points greater the similarity.
Hamad ALshahrani
Hamad ALshahrani on 30 Mar 2020
Write a Matlab program to find an image in the database similar to a query image which it is: a. Not in the database but for the same person. b. Not in the database and for another person. For both cases above, repeat the process 10 times on different faces and compute the accuracy ratio.

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Accepted Answer

Anand
Anand on 18 Mar 2013
BoF seems like a reasonable approach to this problem. Usually, you are working with a much larger set of images than just 5. Crucial to BoF are the feature points and descriptors you use to represent the images as well as the size of the visual vocabulary.
In the example below, I have used SURF features to detect feature points and extract sparse descriptors. I used SURF because they are scale and rotation-invariant. The Computer Vision System Toolbox has functions that enable you to do this:
I then used the K-means algorithm to construct a visual vocabulary using 20 words. This function is part of the Statistics Toolbox. After creating a visual vocabulary, I created a bag-of-features representation for each image using this visual vocabulary. We now have a feature vector of length 20 for each of the 5 images. Applying K-means (again) on this to partition it into 2 sets gives the desired result. The fist four images are partitioned into 1 set and the last image to a different set.
%%Read images
im{1} = rgb2gray(imread('http://i.imgur.com/TMyflBl.png'));
im{2} = rgb2gray(imread('http://i.imgur.com/ozygW2g.png'));
im{3} = rgb2gray(imread('http://i.imgur.com/dGj8FNE.png'));
im{4} = rgb2gray(imread('http://i.imgur.com/VbTLPjG.png'));
im{5} = rgb2gray(imread('http://i.imgur.com/huj2El9.png'));
%%Detect and extract MSER features
detectFeatures = @(in) {detectSURFFeatures(in)};
regions = cellfun(detectFeatures,im);
extractAndTransposeFeatures = @(in,pts) extractFeatures(in,pts)';
features = cellfun(extractAndTransposeFeatures,im,regions,'UniformOutput',false);
figure;
for i = 1 : 5
subplot(2,3,i);
imshow(im{i});
hold on;
plot(regions{i});
end
%%Create a visual vocabulary with 20 words
nWords = 20;
%use kmeans for constructing the vocabulary.
[idx,centers] = kmeans([features{:}]',nWords);
%%BoF Feature Representation
histFtrs = cell(5,1);
for i = 1 : 5
start = 1;
histFtrs{i} = hist(idx(start:start+size(features{i},2)),nWords)';
%normalize
histFtrs{i} = histFtrs{i}./sum(hisFtrs{i});
end
%display histograms
figure;
for i = 1 : 5;
subplot(2,3,i);
bar(histFtrs{i});
end
%%Partition images into 2 sets using kmeans
idx = kmeans([histFtrs{:}]',2)
% idx =
%
% 2
% 2
% 2
% 2
% 1
Hope this helps. Bear in mind that you may need to do a lot more fiddling with some of the parameters and general approach for a more complicated problem.
  3 Comments
Emmanuel Oreoluwa
Emmanuel Oreoluwa on 20 Oct 2014
Hi Anand, Thanks for your clear explanation. Just want to ask if the coordinates of the centers which is 20x64 is for a single image or for the five images?
Mohammad Mortazavi
Mohammad Mortazavi on 10 May 2016
Edited: Mohammad Mortazavi on 10 May 2016
hi! i think your code is little wrong. in below section:
for i = 1 : 5
start = 1;
histFtrs{i} = hist(idx(start:start+size(features{i},2)),nWords)';
%normalize
histFtrs{i} = histFtrs{i}./sum(hisFtrs{i});
end
always (start=1) in all rounds of loop!!! and it counts from begin for all images. i think you should change this code to:
start = 1;
for i = 1 : k
histFtrs{i} = hist(idx(start:start+size(features{i},2)-1),nWords)';
%normalize
histFtrs{i} = histFtrs{i}./sum(histFtrs{i});
start = start+size(features{i},2);
end
now we can count all parts for all images!
i run your code, but my result is different.
my idx=
1
2
2
1
1

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More Answers (3)

Anand
Anand on 23 Mar 2013
save idx.mat idx;
save centers.mat centers;

Mohammed Magdy
Mohammed Magdy on 7 Oct 2013
hello please why did you use this number (2) here (features{i},2)
histFtrs{i} = hist(idx(start:start+size(features{i},2)),nWords)';
  5 Comments
hamed abdulaziz
hamed abdulaziz on 23 Mar 2014
Anand :Thank you,I saw the code above but I divided each image to patches(blocks) and extracted local features from each patch,where is the code doesn't do that,could you guide me with my thanks.

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Yuva
Yuva on 20 Oct 2017
I am doing my final year project on attedance system by face recognition using SIFT algorithm,if there is any code on this project please comment code or provide any link .

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