<p>I would strongly suggest against using frequency domain methods for segmentation:<br>
The nice thing about frequency domain is that it captures non-local properties like textures. However, the price you pay for this global information is the lack of location information: it is very non-trivial to say "where in the picture this frequency comes from". Alas, this location information is <strong>crucial</strong> for segmentation: you must know which pixel belongs to what "frequency"/texture/region... 
There are frequency based descriptors that are designed to maintain some locality information (e.g., Gabor filters etc.). However, these techniques are not easy to implement "out-of-the-box".</p>

<p>Have you considered using deep semantic segmentation methods? If you do not have a lot of labeled images, I would suggest you look at something semi-supervised like <a href="https://arxiv.org/abs/1707.00243" rel="nofollow noreferrer"><em>Ning Xu, Brian Price, Scott Cohen, Jimei Yang, Thomas Huang</em>, <strong>Deep GrabCut for Object Selection</strong> (arXiv 2017)</a>.</p>
segmentation of an image using frequencies
<p>I would like to segment this image <img src="https://i.stack.imgur.com/zS8X6.jpg" alt="image to segment"><br>
(I want only the bees, I have 100 images like that not labelled). I think that the best way to do that is to use frenquency domain because the bees seems to have specific frequencies. But I'm not sure how to do that. How to find the right frequencies ? </p>

<p>Or maybe you think of a better way to do that ? </p>

<p>Thanks in advance !</p>