"Clustering by Composition":
Unsupervised Discovery of Image Categories
Alon Faktor,
Michal Irani
This webpage presents the paper "Clustering by Composition" - Unsupervised Discovery of Image Categories (ECCV 2012).
Paper [PDF]
PAMI extended version [PDF]
Proofs & Additional material [PDF]
Abstract
We define a "good image cluster" as one in which images can be easily
composed (like a puzzle) using pieces from each other, while are difficult to
compose from images outside the cluster. The larger and more statistically significant
the pieces are, the stronger the affinity between the images. This gives
rise to unsupervised discovery of very challenging image categories. We further
show how multiple images can be composed from each other simultaneously and
efficiently using a collaborative randomized search algorithm. This collaborative
process exploits the "wisdom of crowds of images", to obtain a sparse yet meaningful
set of image affinities, and in time which is almost linear in the size of
the image collection. "Clustering-by-Composition" can be applied to very few
images (where a 'cluster model' cannot be 'learned'), as well as on benchmark
evaluation datasets, and yields state-of-the-art results.
Code can be downloaded here
Datasets
Ballet-Yoga tiny dataset [ZIP]
Animals tiny dataset [ZIP]
PASCAL subset [ZIP]