The best paper of ICMR2016 entitled “Pooling Objects for Recognizing Scenes without Examples” by Svetlana Kordumova, Thomas Mensink and Cees Snoek is now available. In this paper we aim to recognize scenes in images without using any scene images as training data. Different from attribute based approaches, we do not carefully select the training classes to match the unseen scene classes. Instead, we propose a pooling over ten thousand of off-the-shelf object classifiers. To steer the knowledge transfer between objects and scenes we learn a semantic embedding with the aid of a large social multimedia corpus. Our key contributions are: we are the first to investigate pooling over ten thousand object classifiers to recognize scenes without examples; we explore the ontological hierarchy of objects and analyze the influence of object classifiers from different hierarchy levels; we exploit object positions in scene images and we demonstrate a new scene retrieval scenario with complex queries. Finally, we outperform attribute representations on two challenging scene datasets, SUNAttributes and Places2.