Dataset issues in object recognition
Jean Ponce, T. Berg, Mark Everingham, D. Forsyth, Martial Hebert, S. Lazebnik, Marcin Marszalek, Cordelia Schmid, B. Russell, A. Torralba, Christopher Williams, Jianguo Zhang and Andrew Zisserman
Toward Category-Level Object Recognition
Appropriate datasets are required at all stages of object recognition research, including learning visual models of object and scene categories, detecting and localizing instances of these models in images, and evaluating the performance of recognition algorithms. Current datasets are lacking in several respects, and this paper discusses some of the lessons learned from existing efforts, as well as innovative ways to obtain very large and diverse annotated datasets. It also suggests a few criteria for gathering future datasets.