Unsupervised Learning of Visual Feature Hierarchies

We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method combines these primitives into high-level abstractions. Our appearance-based learning method uses local statistical analysis between features and Expectation-Maximization to identify and code spatial correlations. Spatial correlation is asserted when two features tend to occur at the same relative position of each other. This learning scheme results in a graphical model that constitutes a probabilistic representation of a flexible visual feature hierarchy. For feature detection, evidence is propagated using Belief Propagation. Each message is represented by a Gaussian mixture where each component represents a possible location of the feature. In experiments, the proposed approach demonstrates efficient learning and robust detection of object models in the presence of clutter and occlusion and under view poin...

• Type: Conference
• Year: 2005
• Where: MLDM
• Authors: Fabien Scalzo, Justus H. Piater
• Keywords: Machine Learning, MLDM 2005, Spatial Correlation, Visual Feature, Visual Feature Hierarchies