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I'm doing image analysis, and I want to classify smooth objects (has smooth boundaries) from non-smooth objects (has zigzag-like boundaries). Which feature should be fed into ML framework? What are some popular techniques for feature extraction of shapes?

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up vote 2 down vote accepted

Fourier shape descriptors are quite easy to use and can do well to differentiate smooth objects from jagged ones. Imagine a polar coordinate system with the origin at the centroid of the 2D object. Store a vector of $r$ values as $\theta$ varies in $[0, 2\pi)$ where $r$ is the distance of the boundary from the centroid at each fixed angle $\theta$. Next, take the Fourier transform (FT) of this vector of $r$ values. If the boundary of the 2D object is jagged, the FT will have lot of non-zeros even at high frequencies. On the other hand, imagine doing this with a perfectly smooth circular object---you will end up with a vector of constant $r$ values whose FT is simply a non-zero DC term.

The DFT vector can be used as a feature vector.

See slides 16-20 in this pdf:

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