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Erschienen in:
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Deep Learning the City: Quantifying Urban Perception at a Global Scale

verfasst von : Abhimanyu Dubey, Nikhil Naik, Devi Parikh, Ramesh Raskar, César A. Hidalgo

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

Computer vision methods that quantify the perception of urban environment are increasingly being used to study the relationship between a city’s physical appearance and the behavior and health of its residents. Yet, the throughput of current methods is too limited to quantify the perception of cities across the world. To tackle this challenge, we introduce a new crowdsourced dataset containing 110,988 images from 56 cities, and 1,170,000 pairwise comparisons provided by 81,630 online volunteers along six perceptual attributes: safe, lively, boring, wealthy, depressing, and beautiful. Using this data, we train a Siamese-like convolutional neural architecture, which learns from a joint classification and ranking loss, to predict human judgments of pairwise image comparisons. Our results show that crowdsourcing combined with neural networks can produce urban perception data at the global scale.

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Metadaten
Titel
Deep Learning the City: Quantifying Urban Perception at a Global Scale
verfasst von
Abhimanyu Dubey
Nikhil Naik
Devi Parikh
Ramesh Raskar
César A. Hidalgo
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46447-3

Electronic ISBN: 978-3-319-46448-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46448-0_12