How to perform an image classification ?
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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!
Answers (6)
Image Analyst
on 14 Apr 2013
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
Anand
on 15 Apr 2013
0 votes
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:
and this function to extract them:
1 Comment
Chaou
on 30 Apr 2013
Image Analyst
on 30 Apr 2013
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.
1 Comment
Image Analyst
on 1 May 2013
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.
1 Comment
Image Analyst
on 5 May 2013
Why don't you just get the mean gray level between certain rows? It looks like that should correlate pretty well with the level of discharge. Of course you'll want to turn off the automatic gain or exposure control in your camera because you can't have the camera trying to reduce the exposure when the image is supposed to be brighter. I can tell you have some sort of automatic gain or exposure because the intensity of the shaft on the bottom is different in the images.
5 Comments
Image Analyst
on 18 May 2013
I don't have that toolbox so I can't replicate it. Type this into the command window:
which detectFASTFeatures
ver
Tell me what it says. If everything looks right, then try casting (the badly-named) I to double.
Chaou
on 18 May 2013
Image Analyst
on 18 May 2013
Edited: Image Analyst
on 19 May 2013
I don't know. I don't have that toolbox. You might have to call them on Monday. Maybe your path is messed up. Did you do anything to your path? Can you search your hard drive in the toolbox subfolder of MATLAB for it?
Chaou
on 20 May 2013
Florian S
on 6 Feb 2017
Perhaps your GPU Driver is too old. I have exactly the same problems. You must have the latest CUDA driver on your running system.
I am searching for a way to perform the 'detectFASTFeatures' script without using the GPU...
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