Learning Human Photo Shooting Patterns from Large-Scale Community Photo Collections

Abstract

Social photo sharing platforms on the Internet (e.g. Flickr) host billions of publicly accessible photos captured by millions of individual users from all over the world. These user-contributed and geo-tagged photo collections provide insights into human sociocultural life and provide important clues for understanding people’s engagement and reaction to places and events around the world today. In this paper, we analyze over 2 million geo-tagged images uploaded by 12,000 individual Flickr users to investigate the photograph shooting patterns of different user groups; that is, tourist and local, Asian and European, and male and female users. Specifically, we make use of visual features extracted on single monocular images and their spatial configurations to infer 3D depth information of the photographs to establish the preferred shooting scale (close-up or far-distant) of the user groups. The results reveal which objects and scenes interest different groups of people and how these preferences change over space and time. As such, the research offers a new approach to the human sciences which study the individual, groups and society.