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I have asked a 'very broad' question yesterday link about build conditional random field based energy function from images. I got negative feedback from the comments and I think I should modify the question and make it more specific.

Here I have got bunch of images with a cow on the grass and some sky at the background. I want to segment the cow from the grass and sky (a toy problem only).

I firstly over-segment the images using some super-pixel method, and I have got the ground truth of labels of my 10 training images. Here is an example,

cow

Then I pass these super-pixel patches to some filter to get texture features and saved them into feature vectors for cow (c), grass (g) and sky (s)

My question is how to using Matlab to implement the conditional probabilities of these three classes?

Like P(X_i|C_c), P(X_i|C_g) and P(X_i|C_s)? X_i are each super-pixel, C_x are the three classes.

I think some webpage mentioned about using Matlab hist function. Not sure how and why to do that. Please give me some basic applicable hints not complicated papers. Thanks a lot. A.

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Samo Jerom
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  • Determine probability that a class appears taking into consideration all super-pixels : P(C), also the probability that a super-pixel with certain qualities/traits (colors, hue, etc.) is part of a certain class : P(X,C). With this you can get P(X|C)... – Falimond Jun 27 '14 at 19:43
  • Compute separate histograms for each of the classes. After that, the conditional probability of a pixel taking on a particular colour given a particular class would simply be the normalized histogram of that class. – rayryeng Jun 28 '14 at 00:13

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