An Analysis of Human-centered Geolocation
| Autor: | Kaili Wang, Luc Van Gool, Yu-Hui Huang, M José Oramas, Tinne Tuytelaars |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Feature extraction Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology 010501 environmental sciences PSI_VISICS Machine learning computer.software_genre Clothing 01 natural sciences Convolutional neural network Task (project management) Visualization Geolocation Margin (machine learning) 0202 electrical engineering electronic engineering information engineering Task analysis 020201 artificial intelligence & image processing Artificial intelligence business computer 0105 earth and related environmental sciences |
| Zdroj: | WACV |
| Popis: | Online social networks contain a constantly increasing amount of images - most of them focusing on people. Due to cultural and climate factors, fashion trends and physical appearance of individuals differ from city to city. In this paper we investigate to what extent such cues can be exploited in order to infer the geographic location, i.e. the city, where a picture was taken. We conduct a user study, as well as an evaluation of automatic methods based on convolutional neural networks. Experiments on the Fashion 144k and a Pinterest-based dataset show that the automatic methods succeed at this task to a reasonable extent. As a matter of fact, our empirical results suggest that automatic methods can surpass human performance by a large margin. Further inspection of the trained models shows that human-centered characteristics, like clothing style, physical features, and accessories, are informative for the task at hand. Moreover, it reveals that also contextual features, e.g. wall type, natural environment, etc., are taken into account by the automatic methods. WACV'18 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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