Solved – SVM for Image Segmentation

feature selectionimage processingimage segmentationsvm

I turn to this forum for advice with the following problem. If you could please shed some light on any aspect of this question I'd be very grateful.

Problem decription:
I'm trying to use an SVM to segment a grayscale image of a puncture in polymer (original res. 1280×1024, can't post, no reputation 🙂

Now, I know this isn't probably the most conventional way to approach this problem, but still I'd like to try whether it is possible in any way.

My work so far:
I think of the segmentation problem as follows:
classify a given pixel based on its value and neighborhood pixel values, i.e. determine whether pixel belongs to the foreground (puncture) or background (anything other than puncture).

I labeled this image using GIMP (for the SVM training purposes, i.e. marked the location of the puncture, i.e. each pixel is given a class (1 – puncture, -1 – background)) and tried to extract some simple features:

  1. central pixel value + neighborhood pixel values with varying size of neighborhood
  2. central pixel value + differences between central pixel value and neighborhood pixel values
  3. central pixel value + 2D FFT spectrum of the neighborhood (amplitude and phase components)
  4. standard deviation of the neighborhood

Note that I varied neighborhood size from 3×3 = 9 dimensions to 11×11 = 121 dimensions. I couldn't go higher (I use MATLAB for this, i'm getting out of memory errors). None of these were found sufficiently discriminatory. (I used PCA to inspect, calculated between-class distance of centroids and their respective class covariance matrices).

Soo, at last, to my question:
Could you think of any useful features to use for this task?
(I was thinking some measures of homogeneity of the neighborhood would be helpful, since inside of the puncture is more or less uniform in brightness, but haven't found any. Also maybe some texture features would be helpful, but who knows.)

Best Answer

In my master thesis I did use (multi class) SVM not on features of a neighborhood but on features of a complete image.

Still, many of the features I used and that are somewhat explained and referenced in my paper may help you (NB. Not peer-reviewed).

Classification of Cell Images Using MPEG-7-influenced Descriptors and Support Vector Machines in Cell Morphology

Perhaps someone else can recommend a good book in image analysis and or computer vision that describes commonly used features?

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