Graph-based predictable feature analysis

PFAx: Predictable Feature Analysis to Perform Control

Predictable Feature Analysis (PFA) [Richthofer and Wiskott, 2015] is an algorithm that performs dimensionality reduction on high dimensional input signal. It extracts those subsignals that are most predictable according to a certain prediction model. We refer to these extracted signals as predictable features. In this work we extend the notion of PFA to take supplementary information into accou...

Feature-Based Graph Visualization

One of the oldest problems in graph drawing is producing layouts of undirected graphs in two and three dimensions. A graph G(V,E) consists of a set of nodes V and a set of relationships or edges E such that E ⊆ V ×V . A drawing or layout of this graph is an assignment of coordinates to the nodes in V . Most of graph drawing has been focused on producing uniform views of this data. These algorit...

Feature Reduction in Graph Analysis

A common approach to improve medical image classification is to add more features to the classifiers; however, this increases the time required for preprocessing raw data and training the classifiers, and the increase in features is not always beneficial. The number of commonly used features in the literature for training of image feature classifiers is over 50. Existing algorithms for selectin...

A Graph-Based Approach to Feature Selection

Feature-Based Stereo Matching Using Graph Cuts

In this paper, we present a novel feature point based stereo matching algorithm with global energy minimization. The initial disparity map is estimated by considering matching SURF key points between two images inside each homogeneous colour region by an adaptive box matching approach. Next, we improve the initial disparity map with a RANSAC based plane fitting technique which relies on accurac...