Image segmentation has traditionally been thought of as a low/mid-level vision process incorporating no high level constraints. However, in complex and uncontrolled environments, such bottom-up strategies have drawbacks that lead to large misclassification rates. Remedies to this situation include taking into account (1) contextual and application constraints, (2) user input and feedback to incrementally improve the performance of the system. Here we attempt to incorporate these in the context of pipeline segmentation in industrial images. This problem is of practical importance for the 3D reconstruction of factory environments. However it poses several fundamental challenges mainly due to shading, highlights and textural variations, etc. Our system performs pipe segmentation by fusing methods from physics-based vision, edge and texture analysis, probabilistic learning and the use of the graph-cut formalism.