Journal article

SUN Database: Exploring a Large Collection of Scene Categories

J Xiao, KA Ehinger, J Hays, A Torralba, A Oliva

International Journal of Computer Vision | SPRINGER | Published : 2016

Abstract

Progress in scene understanding requires reasoning about the rich and diverse visual environments that make up our daily experience. To this end, we propose the Scene Understanding database, a nearly exhaustive collection of scenes categorized at the same level of specificity as human discourse. The database contains 908 distinct scene categories and 131,072 images. Given this data with both scene and object labels available, we perform in-depth analysis of co-occurrence statistics and the contextual relationship. To better understand this large scale taxonomy of scene categories, we perform two human experiments: we quantify human scene recognition accuracy, and we measure how typical each ..

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University of Melbourne Researchers

Grants

Awarded by National Science Foundation


Funding Acknowledgements

We thank Yinda Zhang for help on the scene classification experiments. This work is funded by Google Research Award to J. X., NSF Grant 1016862 to A. O, NSF CAREER Award 0747120 to A. T., NSF CAREER Award 1149853 to J.H, as well as ONR MURI N000141010933, Foxconn and gifts from Microsoft and Google. K.A.E was funded by a NSF Graduate Research fellowship.