The time of capture of consumer photos provides rich information in temporal context and has been widely employed for solving various multimedia problems, such as multimedia retrieval and social media analysis. However, we observed that the recorded time stamp in a consumer photo does not often correspond to the true local time at which the photo was taken. This would greatly damage the robustness of time-aware multimedia applications, such as travel route recommendation. Therefore, motivated by the use of traditional sundials, this work proposes a system, Photo Sundial, for estimating the time of capture by exploiting the astronomical theory. In particular, we infer the time by establishing its relations to the measurable astronomical factors from a given outdoor photo, i.e. the sun position in the sky and the camera viewing direction in the photo-taken location. In practice, since it is more often that people would take multiple photos in a single trip, we further develop an optimization framework to jointly estimate the time from multiple photos. Experimental results show that the average estimated time error is less than 0.9 h by the proposed approach, with a significant 65% relative improvement compared to the state-of-the-art method (2.5 h). To the best of our knowledge, this work is the first study in multimedia research to explicitly address the problem of time of capture estimation in consumer photos, and the achieved performances highly encourage our system for practical applications.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence