@inproceedings{a7b85677f0cc415d89e234fead90610b,
title = "Automatic photo orientation detection with convolutional neural networks",
abstract = "We apply convolutional neural networks (CNN) to the problem of image orientation detection in the context of determining the correct orientation (from 0, 90, 180, and 270 degrees) of a consumer photo. The problem is especially important for digitazing analog photographs. We substantially improve on the published state of the art in terms of the performance on one of the standard datasets, and test our system on a more difficult large dataset of consumer photos. We use Guided Backpropagation to obtain insights into how our CNN detects photo orientation, and to explain its mistakes.",
keywords = "convolutional neural networks, guided backpropagation, image orientation, photo, visualizing convnets",
author = "Ujash Joshi and Michael Guerzhoy",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 14th Conference on Computer and Robot Vision, CRV 2017 ; Conference date: 17-05-2017 Through 19-05-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/CRV.2017.59",
language = "English (US)",
series = "Proceedings - 2017 14th Conference on Computer and Robot Vision, CRV 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "103--108",
booktitle = "Proceedings - 2017 14th Conference on Computer and Robot Vision, CRV 2017",
address = "United States",
}