Accurate Binary Image Selection from Inaccurate User Input

Abstract : Selections 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 a 100% 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.
Complete list of metadatas

Cited literature [22 references]  Display  Hide  Download


https://hal.inria.fr/hal-00782232
Contributor : Cyril Soler <>
Submitted on : Tuesday, January 29, 2013 - 1:15:35 PM
Last modification on : Wednesday, April 11, 2018 - 1:59:46 AM
Long-term archiving on : Monday, June 17, 2013 - 5:07:08 PM

Files

sel.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Kartic Subr, Sylvain Paris, Cyril Soler, Jan Kautz. Accurate Binary Image Selection from Inaccurate User Input. Computer Graphics Forum, Wiley, 2013, 32 (2pt1), pp.41-50. ⟨10.1111/cgf.12024⟩. ⟨hal-00782232⟩

Share

Metrics

Record views

1102

Files downloads

1393