Object recognition in small images
R. Fergus, A. Torralba

The human visual system is remarkably tolerant to degradations in image resolution: in a scene recognition task, the performance of subjects is similar whether 32x32 color images or multi-mega pixel images are used. Accordingly, we consider the task of scene recognition in computer vision using very small (32x32 pixel) images. Small images force us to employ contextual reasoning and to explore new representations due to the lack of textural cues on which many existing algorithms are based. However, the small size of each image carries two important benefits: (i) it permits standard machine learning tools to be easily applied and (ii) huge image databases may be easily collected.
Inspired by the data-driven nature of Google, we deploy simple data mining methods to a large collection of images (50 million). We will show very preliminary results.

presentation