Unsupervised Learning of Image Manifolds by Semidefinite Programming

Unsupervised Image Partitioning with Semidefinite Programming

Unsupervised Learning of Shape Manifolds

Classical shape analysis methods use principal component analysis to reduce the dimensionality of shape spaces. The basic assumption behind these methods is that the subspace corresponding to the major modes of variation for a particular class of shapes is linearised. This may not necessarily be the case in practice. In this paper, we present a novel method for extraction of the intrinsic param...

Hierarchical Image Segmentation Based on Semidefinite Programming

Image segmentation based on graph representations has been a very active field of research recently. One major reason is that pairwise similarities (encoded by a graph) are also applicable in general situations where prototypical image descriptors as partitioning cues are no longer adequate. In this context, we recently proposed a novel convex programming approach for segmentation in terms of o...

Image partitioning based on semidefinite programming

Unsupervised Representation Learning by Predicting Image Rotations

Over the last years, deep convolutional neural networks (ConvNets) have transformed the field of computer vision thanks to their unparalleled capacity to learn high level semantic image features. However, in order to successfully learn those features, they usually require massive amounts of manually labeled data, which is both expensive and impractical to scale. Therefore, unsupervised semantic...