We propose TT-Seg, an unsupervised image segmentation framework that employs Tensor Train (TT) decomposition and probabilistic tensor sampling to optimize Quadratic Unconstrained Binary Optimization (QUBO) problems. TT-Seg achieves segmentation performance comparable to classical solvers while offering enhanced scalability. Experimental results indicate that the TT-based approach performs effectively on small-scale problems, although for larger QUBO instances, leading solvers such as Gurobi and the D-Wave hybrid solver remain superior.