Many state-of-the-art segmentation algorithms rely on Markov or Conditional Random Field models designed to enforce spatial and global consistency constraints. This is often accom...
Aurelien Lucchi, Yunpeng Li, Xavier Boix, Kevin Sm...
We study the challenging problem of localizing and classifying category-specific object contours in real world images. For this purpose, we present a simple yet effective method ...
Bharath Hariharan, Pablo Arbelaez, Lubomir Bourdev...
This paper proposes a very general max-margin learning framework for distance-based clustering. To this end, it formulates clustering as a high order energy minimization problem w...
It has been shown repeatedly that iterative relevance feedback is a very efficient solution for content-based image retrieval. However, no existing system scales gracefully to hu...
In this paper, we present an analytical method for computing the globally optimal estimates of orthogonal vanishing points in a “Manhattan world” with a calibrated camera. We ...