Can computer vision problems benefit from structured hierarchical classification?

How cognitive modeling can benefit from hierarchical Bayesian models

Hierarchical Bayesian modeling provides a flexible and interpretable way of extending simple models of cognitive processes. To introduce this special issue, we discuss four of the most important potential hierarchical Bayesian contributions. The first involves the development of more complete theories, including accounting for variation coming from sources like individual differences in cogniti...

Structured Learning and Prediction in Computer Vision

Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structure reflecting task-specific relations and constraints. This tutorial introduces the reader to the most popular classes of structured models in computer vision. Our focus is discrete undirected graphical models which we ...

Hierarchical Convolutional Deep Learning in Computer Vision

It has long been the goal in computer vision to learn a hierarchy of features useful for object recognition. Spanning the two traditional paradigms of machine learning, unsupervised and supervised learning, we investigate the application of deep learning methods to tackle this challenging task and to learn robust representations of images. We begin our investigation with the introduction of a n...

Hierarchical curvature estimation in computer vision

On Graph-Structured Discrete Labelling Problems in Computer Vision: Learning, Inference and Applications

A number of problems in computer vision (e.g., image segmentation, gender classification of faces, etc) can be formulated as graph-structured discrete labelling problems, where the goal is to predict labels (e.g. foreground/background, male/female) for a set of variables (e.g. pixels, faces in an image, etc) that have some known underlying structure (e.g., neighbouring pixels in an image often ...