Unsupervised Discovery of Mid-Level Discriminative Patches

Discriminative Patches, Unsupervised Discovery

Teaser Image


An algorithm to automatically discover discriminative patterns in large corpuses of images. These patterns could be used as features to represent images. Visit website for more information.


The goal of this paper is to discover a set of discriminative patches which can serve as a fully unsupervised mid-level visual representation. The desired patches need to satisfy two requirements: 1) to be representative, they need to occur frequently enough in the visual world; 2) to be discriminative, they need to be different enough from the rest of the visual world. The patches could correspond to parts, objects, “visual phrases”, etc. but are not restricted to be any one of them. We pose this as an unsupervised discriminative clustering problem on a huge dataset of image patches. We use an iterative procedure which alternates between clustering and training discriminative classifiers, while applying careful cross-validation at each step to prevent overfitting. The paper experimentally demonstrates the effectiveness of discriminative patches as an unsupervised mid-level visual representation, suggesting that it could be used in place of visual words for many tasks. Furthermore, discrim- inative patches can also be used in a supervised regime, such as scene classification, where they demonstrate state-of-the-art performance on the MIT Indoor-67 dataset.



Paper: ECCV 2012 Pdf (7.5 MB)
Poster: ECCV 2012 Pdf (28.2 MB)


Saurabh Singh, Abhinav Gupta and Alexei A. Efros. Unsupervised Discovery of Mid-Level Discriminative Patches. In European Conference on Computer Vision (2012).

Visit Arxiv Entry (http://arxiv.org/abs/1205.3137)


  author = {Saurabh Singh and Abhinav Gupta and Alexei A. Efros},
  title = {Unsupervised Discovery of Mid-level Discriminative Patches},
  booktitle={European Conference on Computer Vision},
  year = {2012},
  eprint= {1205.3137},
  archivePrefix = {arXiv},
  primaryClass = {cs.CV},
  url = {http://arxiv.org/abs/1205.3137},


Code is available on Github


Discovered discriminative patches for the Pascal 2007 subset used in the paper and MIT Indoor-67 dataset are here (331MB).

Related Papers

What Makes Paris Look like Paris?, Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Sivic, and Alexei A. Efros. in ACM Transactions on Graphics, SIGGRAPH 2012


This research is supported by: