  Image segmentation is a very popular and important task in computer vision.
In this paper, inverse quantum Fourier transform (IQFT) for image segmentation
has been explored and a novel IQFT-inspired algorithm is proposed and
implemented by leveraging the underlying mathematical structure of the IQFT.
Specifically, the proposed method takes advantage of the phase information of
the pixels in the image by encoding the pixels' intensity into qubit relative
phases and applying IQFT to classify the pixels into different segments
automatically and efficiently. To the best of our knowledge, this is the first
attempt of using IQFT for unsupervised image segmentation. The proposed method
has low computational cost comparing to the deep learning-based methods and
more importantly it does not require training, thus make it suitable for
real-time applications. The performance of the proposed method is compared with
K-means and Otsu-thresholding. The proposed method outperforms both of them on
the PASCAL VOC 2012 segmentation benchmark and the xVIEW2 challenge dataset by
as much as 50% in terms of mean Intersection-Over-Union (mIOU).
