Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
AutoAlbum: Clustering Digital Photographs using Probabilistic Model Merging
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Using Mechanical Turk to build machine translation evaluation sets
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
Summarization of personal photologs using multidimensional content and context
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Automated event clustering and quality screening of consumer pictures for digital albuming
IEEE Transactions on Multimedia
Generating ground truth for music mood classification using mechanical turk
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Hidden Markov Model for Event Photo Stream Segmentation
ICMEW '12 Proceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops
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Personal digital photo libraries embody a large amount of information useful for research into photo organization, photo layout, and development of novel photo browser features. Even when anonymity can be ensured, amassing a sizable 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 data from 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 a viable means to acquire massive data collections for researchers.