Robust Inverse Quantum Fourier Transform Inspired Algorithm for Unsupervised Image Segmentation

Image segmentation is a crucial task in computer vision. In this study, we propose a method based on inverse quantum Fourier transform (IQFT) and develop a robust IQFT-inspired algorithm for unsupervised image segmentation. The proposed method leverages the underlying mathematical mechanism of the IQFT to cluster the input image pixels automatically and efficiently into different segments. Specifically, by considering the correlation between the within-cluster mean sum of squared error (MSSE) and the probability of quantum measurements, the proposed robust algorithm significantly improves the segmentation performance. It is an unsupervised method with characteristics similar to k-means, i.e., the proposed method does not require training. Extensive evaluation of the proposed method has been carried out, showing that it outperforms the classical k-means on the PASCAL VOC 2012 segmentation benchmark by as much as 4.21%, the Flowers dataset by as much as 4.4%, and the xVIEW2 challenge dataset by as much as 11.1% in terms of Intersection-Over-Union (IoU). It is also demonstrated that the proposed method has comparable or mixed performance compared to recent more complex approaches. However, compared to approaches such as GrabCut, which require a measure of the user interaction, and deep learning-based methods, which require generative models and deep feature extraction algorithms, the proposed method does not require training or user involvement. This makes it a promising choice for applications that do not have access to data before deployment or have very limited training data.