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
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.