KNN Matting

By Qifeng Chen, Dingzeyu Li, Chi-Keung Tang. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013

490 citations recorded by the source corpus.

We are interested in a general alpha matting approach for the simultaneous extraction of multiple image layers; each layer may have disjoint segments for material matting not limited to foreground mattes typical of natural image matting. The estimated alphas also satisfy the summation constraint. Our approach does not assume the local colorline model, does not need sophisticated sampling strategies, and generalizes well to any color or feature space in any dimensions. Our matting technique, aptly called KNN matting, capitalizes on the nonlocal principle by usingK nearest neighbors (KNN) in matching nonlocal neighborhoods, and contributes a simple and fast algorithm giving competitive results with sparse user markups. KNN matting has a closed-form solution that can leverage on the preconditioned conjugate gradient method to produce an efficient implementation. Experimental evaluation on benchmark datasets indicates that our matting results are comparable to or of higher quality than state of the art methods.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e45bb081cd31c24c5f89c6a8a1489671c8bbb109

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In Computer Vision and Pattern Recognition

Citations in the corpus

Real-Time High-Resolution Background Matting (2020, background): “Traditional (non-learning based) matting algorithms [6, 16, 17, 29, 9, 2, 7] require manual annotation (a trimap) and solve for the alpha matte in the ‘unknown’ region of the trimap.”
Deep Image Matting (2017, methodology): “Other propagation methods include random walks [14], solving Poisson equations [31], and nonlocal propagation methods [21, 7, 5].”