“Clustering by Composition” for Unsupervised Discovery of Image Categories
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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' can not be 'learned') as well as on benchmark evaluation datasets, and yields state-of-the-art results.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !