Unsupervised learning of visual taxonomies

Unsupervised learning of visual taxonomies,10.1109/CVPR.2008.4587620,Evgeniy Bart,Ian Porteous,Pietro Perona,Max Welling

Unsupervised learning of visual taxonomies   (Citations: 16)
BibTex | RIS | RefWorks Download
As more images and categories become available, orga- nizing 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 im- age 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 im- ages 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 taxon- omy allows good image categorization and, in this respect, is superior to the popular LDA model.
Cumulative Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.
Sort by: