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Unsupervised Learning of Visual Feature Hierarchies
[chapter]
2005
Lecture Notes in Computer Science
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
doi:10.1007/11510888_24
fatcat:4oll3hyhqjgajaysi7azx2hgv4