AbstractSelections are central to image editing, e.g., they are the starting point of common operations such as copy‐pasting and local edits. Creating them by hand is particularly tedious and scribble‐based techniques have been introduced to assist the process. By interpolating a few strokes specified by users, these methods generate precise selections. However, most of the algorithms assume a100% accurate input, and even small inaccuracies in the scribbles often degrade the selection quality, which imposes an additional burden on users. In this paper, we propose a selection technique tolerant to input inaccuracies. We use a dense conditional random field (CRF) to robustly infer a selection from possibly inaccurate input. Further, we show that patch‐based pixel similarity functions yield more precise selection than simple point‐wise metrics. However, efficiently solving a dense CRF is only possible in low‐dimensional Euclidean spaces, and the metrics that we use are high‐dimensional and often non‐Euclidean. We address this challenge by embedding pixels in a low‐dimensional Euclidean space with a metric that approximates the desired similarity function. The results show that our approach performs better than previous techniques and that two options are sufficient to cover a variety of images depending on whether the objects are textured.