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Automatic Object Segmentation by Quantum Cuts

In this study, the link between quantum mechanics and graph-cuts is exploited and a novel saliency map generation and salient object segmentation method is proposed based on the ground state solution of a modified Hamiltonian. First, the graph representation of certain quantum mechanical operators is studied. This reveals strong connections with widely used graph-cut algorithms while quantum mechanical constraints exhibit crucial advantages over the existing graph-cut algorithms. Furthermore, concepts such as potential field helps solving a particular singularity problem related to Laplacian matrices. In the proposed approach, the ground state (wave function) corresponding to a sub-atomic particle of a modified Hamiltonian operator corresponds to a particular optimization problem, the solution of which yields the salient object segmentation in a digital image. This approach provides a parameter-free -hence dataset independent-, unsupervised and fully automatic saliency map generation, which outperforms many existing state-of-the-art algorithms. The results of the proposed salient object extraction method exhibit such a promising accuracy that pushes the frontier in this field to the borders of the input-driven processing only - without the use of "object knowledge" aided by long-term human memory and intelligence. Furthermore, with the novel technologies for measuring a quantum wave function, the proposed method has a unique potential: Salient object segmentation in an actual physical setup in nano-scale. Such an unprece-dendent property will not only produce segmentation results instantaneously, but may be a unique opportunity to achieve accurate object segmentation in real-time for the massive visual repositories of today's "Big Data".