How to perform an image classification ?

Hello. I have plenty of images, each one of them corresponds to a class. Knowing that I have 3 classes, I want to perform an image classification. I'm used to SVM and others, and I know how to perform the training and classification.

How do I proceed to the feature extraction from an image ?

Thank you!

Answer 1

Once you have the classified image, you essentially have a labeled image. 0 = background, a value of 1 = class 1, and so on. If you want all blobs of a certain class to be measured as a group, then just call regionprops.

groupMeasurements = regionprops(classifiedImage, 'all');

If you want each blob for a certain class to be measured by itself, then turn it into a binary image and then call regionprops:

binaryImage = classifiedImage == theClassNumberYouWant;

Then call regionprops:

individualBlobMeasurements = regionprops(binaryImage, 'all');

See my "BlobsDemo" for a more comprehensive tutorial: http://www.mathworks.com/matlabcentral/fileexchange/?term=authorid%3A31862

Answer 2

There are a bunch of different feature extraction approaches that are tied to what kind of images you have and the kind of problem you are attempting to solve. Here are some common feature descriptors you could extract: SURF features,MSER regions,FAST corners, Minimum Eigen Value corners and Harris corners.

Guess what, they are all in the Computer Vision System Toolbox!

Use the following functions to detect them:

detectFASTFeatures

detectSURFFeatures

detectHarrisFeatures

detectMSERFeatures

detectMinEigenFeatures

and this function to extract them:

extractFeatures

Thank you for your answers! I'm dealing with this error when I run detectFASTFeatures, or any else command cited above. I put my image in the variable 'x'. He is the error:

>> size(x)

ans =

   576   720     3
>> corners = detectFASTFeatures(x); Undefined function 'detectFASTFeatures' for input arguments of type 'uint8'.

Can you help me, please?

Answer 3

Did you see the help:

"points = detectFASTFeatures(I) returns a cornerPoints object, points. The object contains information about the feature points detected in a 2-D grayscale input image, I. "

You have a 3D image - a color image. The help says that it needs to be a 2D grayscale image. Either take one color channel

grayImage = x(:,:,2);

or a weighted average of the color channels:

grayImage = rgb2gray(x);

The first method is preferable.

Answer 4

Hello. This is an example of 3-D images I want to work on. This image described an electrical discharge. Basically, I want to make an image recognition system able to detect near occurring discharge (as in this image), or not.

When I take one color channel, by runing this command:

grayImage = x(:,:,2);

I obtain this image:

Here are some problems, First, the 2-D image obtained does not describe the electrical discharge. Second one, even if I run this cornerPoints of the 2-D image, it still not working. It displays this message again:

*>> points = detectFASTFeatures(grayImage)* Undefined function 'detectFASTFeatures' for input arguments of type 'uint8'.

Even if I use an image from matlab library, just like this:

*>> I = imread('cameraman.tif');* *>> corners = detectFASTFeatures(I);* Undefined function 'detectFASTFeatures' for input arguments of type 'uint8'.

Can you help me, please? I would really appreciate it! :)

That pseudocolored image does not make sense. Even ignoring the colormap it doesn't look anything like what it should. You should see a bright band running through that glass slide or panel or whatever it is. What does it look like if you do

imshow(grayImage, []);

Plus you need to upload an image with no discharge, and one with full discharge, and maybe one or two more with "nearly occurring discharge" so I can see what it is that distinguishes the nearly occurring discharge from the full or no discharge image.