CODEWITHSUNDEEP
matlabcomputer-visionsurfmatlab-cvstcbir
Ahmed Tarawneh
Ahmed Tarawneh

Reputation: 51

SURF Descriptor and Matching for Multiple Image in Matlab

I'm working on Content based image retrieval project using matlab when I apply the function point=detectSURFFeatures(image) I get 83*1 surf point that has the following information:

         `Scale: [83x1 single]
          SignOfLaplacian: [83x1 int8]
          Orientation: [83x1 single]
          Location: [83x2 single]
          Metric: [83x1 single]
          Count: 83`

I need to know how I can extract a (distinctive and Fixed) feature vector that represents each image in a database that contains thousands of images,any help please ?
Here is the samples of the database. (Wang database)

Upvotes: 1

Views: 2857

Answers (2)

Dima
Dima

Reputation: 39389

First of all, detectSURFFeatures only gives you the interest point locations, scales, and orientations. You also have to call extractFeatures, which will give you the SURF descriptors, that are vectors describing the image patch around each interest point.

Now, you are trying to convert a set of patch descriptors representing an image into a single vector, and there are multiple ways to do that. A popular approach is called bag of features, aka bag of visual words.

Upvotes: 1

krisdestruction
krisdestruction

Reputation: 1960

Currently, I am using imread to read the image as follows. Matlab's detectSURFFeatures function only works on greyscale images.

I = rgb2gray( imread( '434.jpg' ) );

You can run this line to get the SURF features.

points = detectSURFFeatures( I );

You can plot the surf features using the following.

imshow( I );
hold on;
plot( point.selectStrongest(10) );
hold off;

Here is the visualization of the image I worked with.

enter image description here

This printing the points object, you can get the following properties showing 41 features.

          Scale: [41x1 single]
SignOfLaplacian: [41x1 int8]
    Orientation: [41x1 single]
       Location: [41x2 single]
         Metric: [41x1 single]
          Count: 41

If you have all the grayscale images stored in a cell object called cellimg (one cell element for each image), you can run detectSURFFeatures on each one as follows.

cellsurf = cellfun( @(I) detectSURFFeatures( I ), cellimg, 'UniformOutput', false );

Each element of cellsurf will contain SURF points. Since you want a set of distinctive and fixed features that will identify each image, you can select the strongest points on each image in cellsurf. You can either use the top n number of features or set n = min( points ). Calculate the min number of features using the following code.

n = min( cellfun( @(S) S.Count, cellsurf ) );

Then you can select the strongest points by running selectStrongest on each cell in cellsurf.

F = cellfun( @(S) S.selectStrongest( n ), cellsurf, 'UniformOutput', false);

The variable F will contain a constant set of features. You can change n accordingly to change the number of strongest features you want. To match two sets of features, you can use the builtin matchFeatures function.

Notes

  • If you need more features, you can specify a different 'MetricThreshold' parameter when calling the detectSURFFeatures function.
  • You can use other feature algorithms instead of SURF by using the functions: detectBRISKFeatures, detectFASTFeatures, detectHarrisFeatures, detectMinEigenFeatures, detectMSERFeatures

Upvotes: 1

Related Questions