Learning Deep Features for Scene Recognition using Places Database
Name
places_NIPS14.pdf
Size
2 MB
Format
Adobe PDF
Checksum (MD5)
edcb2f1bf642bfa7cfaeeea74b8ccbad
Author(s)
Zhou, Bolei
Lapedriza Garcia, Agata
Xiao, Jianxiong
Torralba, Antonio
Oliva, Aude
Date Issued
2014
Journal
Advances in Neural Information Processing Systems (NIPS) 27
Publisher
Neural Information Processing Systems Foundation
Citation
Zhou, 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 (NIPS) 27, 2014.
Version
Final published version
Abstract
Scene 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://papers.nips.cc/paper/5349-learning-deep-features-for-scene-recognition-using-places-database
