Constructing an Anonymous Dataset From the Personal Digital Photo Libraries of Mac App Store Users
Research Area: Digital Libraries Year: 2013
Type of Publication: In Proceedings  
Authors:
  • Jesse Prabawa Gozali
  • Min-Yen Kan
  • Hari Sundaram
 
   
Note:
Short Paper
Abstract:
Personal digital photo libraries embody a large amount of information useful for research into photo organization, photo layout, and the development of novel photo browser features. Even when anonymity can be ensured, amassing a sizeable dataset from these libraries is still difficult due to the visibility and cost that would be required from such a study. We explore using the Mac App Store to reach more users to collect such personal digital photo libraries. More specifically, we compare and discuss how it differs from common data collection methods — e.g. Amazon Mechanical Turk — in terms of time, cost, quantity, and design of the data collection application. We have collected a large, openly available photo feature dataset using this manner. We illustrate the types of data that can be collected. In 60 days, we collected data from 20,778 photo sets (473,772 photos). Our study with the Mac App Store suggests that popular application distribution channels is viable means to acquire massive data collections for researchers.
Digital version