Tensor completion for estimating missing values in visual data

In this paper we propose an algorithm to estimate missing values in tensors of visual data. The values can be missing due to problems in the acquisition process, or because the user manually identified unwanted outliers. Our algorithm works even with a small amount of samples and it can propagate structure to fill larger missing regions. Our methodology is built on recent studies about matrix completion using the matrix trace norm. The contribution of our paper is to extend the matrix case to the tensor case by laying out the theoretical foundations and then by building a working algorithm. First, we propose a definition for the tensor trace norm, that generalizes the established definition of the matrix trace norm. Second, similar to matrix completion, the tensor completion is formulated as a convex optimization problem. Unfortunately, the straightforward problem extension is significantly harder to solve than the matrix case because of the dependency among multiple c...

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Ji Liu, Przemyslaw Musialski, Peter Wonka, Jieping Ye
• Keywords: Computer Vision, ICCV 2009, Tensors Of Visual Data