Novelty detection is the problem of identifying whether a new data point is considered to be an inlier or an outlier. We assume that training data is available to describe only the inlier distribution. Recent approaches primarily leverage deep encoder-decoder network architectures to compute a reconstruction error that is used to either compute a novelty score or to train a one-class classifier. While we too leverage a novel network of that kind, we take a probabilistic approach and effectively compute how likely it is that a sample was generated by the inlier distribution. We achieve this with two main contributions. First, we make the computation of the novelty probability feasible because we linearize the parameterized manifold capturing the underlying structure of the inlier distribution, and show how the probability factorizes and can be computed with respect to local coordinates of the manifold tangent space. Second, we improve the training of the autoencoder network. An extensive set of results show that the approach achieves state-of-the-art performance on several benchmark datasets.

We address the problem of detecting and recognizing online the occurrence of human interactions as seen by a network of multiple cameras. We represent interactions by forming temporal trajectories, coupling together the body motion of each individual and their proximity relationships with others, and also sound whenever available. Such trajectories are modeled with kernel state-space (KSS) models. Their advantage is being suitable for the online interaction detection, recognition, and also for fusing information from multiple cameras, while enabling a fast implementation based on online recursive updates. For recognition, in order to compare interaction trajectories in the space of KSS models, we design so-called pairwise kernels with a special symmetry. For detection, we exploit the geometry of linear operators in Hilbert space, and extend to KSS models the concept of parity space, originally defined for linear models. For fusion, we combine KSS models with kernel construction and multiview learning techniques. We extensively evaluate the approach on four single view publicly available data sets, and we also introduce, and will make public, a new challenging human interactions data set that we have collected using a network of three cameras. The results show that the approach holds promise to become an effective building block for the analysis of real-time human behavior from multiple cameras.

Sparse representation and low-rank matrix decomposition approaches have been successfully applied to several computer vision problems. They build a generative representation of the data, which often requires complex training as well as testing to be robust against data variations induced by nuisance factors. We introduce the invariant components, a discriminative representation invariant to nuisance factors, because it spans subspaces orthogonal to the space where nuisance factors are defined. This allows developing a framework based on geometry that ensures a uniform inter-class separation, and a very efficient and robust classification based on simple nearest neighbor. In addition, we show how the approach is equivalent to a local metric learning, where the local metrics (one for each class) are learned jointly, rather than independently, thus avoiding the risk of overfitting without the need for additional regularization. We evaluated the approach for face recognition with highly corrupted training and testing data, obtaining very promising results.