Patch Alignment Manifold Matting

Image Matting via LLE/iLLE Manifold Learning

Accurately extracting foreground objects is the problem of isolating the foreground in images and video, called image matting which has wide applications in digital photography. This problem is severely ill-posed in the sense that, at each pixel, one must estimate the foreground and background pixels and the so-called alpha value from only pixel information. The most recent work in natural imag...

Non-parametric Patch based Video Matting

In computer vision, matting is the process of extracting foreground objects while preserving their pixel-wise coverage in the scene. This coverage is referred to as opacity or alpha matte. Once an accurate alpha matte is estimated, a foreground object can be seamlessly composited onto a new background. In this paper we present a novel patch based non-parametric approach for video matting. The t...

Manifold Alignment

Manifold Warping: Manifold Alignment over Time

Knowledge transfer is computationally challenging, due in part to the curse of dimensionality, compounded by source and target domains expressed using different features (e.g., documents written in different languages). Recent work on manifold learning has shown that data collected in real-world settings often have high-dimensional representations, but lie on low-dimensional manifolds. Furtherm...

Generalized Unsupervised Manifold Alignment

In this paper, we propose a Generalized Unsupervised Manifold Alignment (GUMA) method to build the connections between different but correlated datasets without any known correspondences. Based on the assumption that datasets of the same theme usually have similar manifold structures, GUMA is formulated into an explicit integer optimization problem considering the structure matching and preserv...