Skip to main content

Learning Deep Features for Scene Recognition using Places Database

Author(s): Zhou, Bolei; Lapedriza, Agata; Xiao, Jianxiong; Torralba, Antonio; Oliva, Aude

Download
To refer to this page use: http://arks.princeton.edu/ark:/88435/pr14v8w
Full metadata record
DC FieldValueLanguage
dc.contributor.authorZhou, Bolei-
dc.contributor.authorLapedriza, Agata-
dc.contributor.authorXiao, Jianxiong-
dc.contributor.authorTorralba, Antonio-
dc.contributor.authorOliva, Aude-
dc.date.accessioned2021-10-08T19:49:21Z-
dc.date.available2021-10-08T19:49:21Z-
dc.date.issued2014en_US
dc.identifier.citationZhou, Bolei, Agata Lapedriza, Jianxiong Xiao, Antonio Torralba, and Aude Oliva. "Learning Deep Features for Scene Recognition using Places Database." Advances in Neural Information Processing Systems 27 (2014).en_US
dc.identifier.issn1049-5258-
dc.identifier.urihttps://proceedings.neurips.cc/paper/2014/file/3fe94a002317b5f9259f82690aeea4cd-Paper.pdf-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr14v8w-
dc.description.abstractScene recognition is one of the hallmark tasks of computer vision, allowing definition of a context for object recognition. Whereas the tremendous recent progress in object recognition tasks is due to the availability of large datasets like ImageNet and the rise of Convolutional Neural Networks (CNNs) for learning high-level features, performance at scene recognition has not attained the same level of success. This may be because current deep features trained from ImageNet are not competitive enough for such tasks. Here, we introduce a new scene-centric database called Places with over 7 million labeled pictures of scenes. We propose new methods to compare the density and diversity of image datasets and show that Places is as dense as other scene datasets and has more diversity. Using CNN, we learn deep features for scene recognition tasks, and establish new state-of-the-art results on several scene-centric datasets. A visualization of the CNN layers' responses allows us to show differences in the internal representations of object-centric and scene-centric networks.en_US
dc.language.isoen_USen_US
dc.relation.ispartofAdvances in Neural Information Processing Systemsen_US
dc.rightsFinal published version. Article is made available in OAR by the publisher's permission or policy.en_US
dc.titleLearning Deep Features for Scene Recognition using Places Databaseen_US
dc.typeConference Articleen_US
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceedingen_US

Files in This Item:
File Description SizeFormat 
LearningFeaturesScene.pdf2.05 MBAdobe PDFView/Download


Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.