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, discriminative
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.

本文旨在发现一组具有判别性的补丁，用作完全无监督的中级视觉表示，通过对图像补丁数据集进行无监督聚类和训练判别分类器来证明其有效性，进而能用于许多任务中代替视觉单词，并且在 MIT Indoor-67 数据集上展示了其在场景分类等任务中的最新性能。