Unsupervised Learning of Visual Taxonomies
As more images and categories become available, organizing them becomes
crucial. We present a novel statistical method for organizing a collection
of images into a tree-shaped hierarchy. The method employs a non-parametric
Bayesian model and is completely unsupervised. Each image is associated
with a path through a tree. Similar images share initial segments of their
paths and therefore have a smaller distance from each other. Each internal
node in the hierarchy represents information that is common to images whose
paths pass through that node, thus providing a compact image
representation. Our experiments show that a disorganized collection of
images will be organized into an intuitive taxonomy. Furthermore, we find
that the taxonomy allows good image categorization and, in this respect, is
superior to the popular LDA model.
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Text Reference
Evgeniy Bart, Ian Porteous, Pietro Perona, and Max Welling. Unsupervised learning of visual taxonomies. In CVPR. 2008.BibTeX Reference
@inproceedings{BartPPW_CVPR_2008,AUTHOR = "Bart, Evgeniy and Porteous, Ian and Perona, Pietro and Welling, Max",
TITLE = "Unsupervised Learning of Visual Taxonomies",
BOOKTITLE = "CVPR",
YEAR = "2008",
TAG = "grouping"
}