MMM2: mobile media metadata for media sharing

Authors:
Marc Davis;John Canny;Nancy Van House;Nathan Good;Simon King;Rahul Nair;Carrie Burgener;Bruce Rinehart;Rachel Strickland;Guy Campbell;Scott Fisher;Nick Reid
Affiliations:
University of California at Berkeley, Berkeley, CA;University of California at Berkeley, Berkeley, CA;University of California at Berkeley, Berkeley, CA;University of California at Berkeley, Berkeley, CA;University of California at Berkeley, Berkeley, CA;University of California at Berkeley, Berkeley, CA;University of California at Berkeley, Berkeley, CA;University of California at Berkeley, Berkeley, CA;University of California at Berkeley, Berkeley, CA;University of California at Berkeley, Berkeley, CA;University of California at Berkeley, Berkeley, CA;University of California at Berkeley, Berkeley, CA
Venue:
Proceedings of the 13th annual ACM international conference on Multimedia
Year:
2005

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Abstract

As cameraphones become the dominant platform for consumer multimedia capture worldwide, multimedia researchers are faced both with the challenge of how to help users manage the billions of photographs they are collectively producing and the opportunity to leverage cameraphones' ability to automatically capture temporal, spatial, and social contextual metadata to help manage consumer multimedia content. In our Mobile Media Metadata 2 (MMM2) prototype, we apply collaborative filtering techniques to automatically gathered contextual metadata to infer the likely sharing recipients for photos captured on cameraphones. We show that while current cameraphone sharing interfaces are fraught with difficulty, it is possible to use a context-aware approach to make the sharing of cameraphone photos simpler and more satisfying for users. Based on our analysis of the relative contributions of different cameraphone sensors to predicting the likely recipients for photos, we discover for our user population that the temporal context of photo capture proved highly predictive of photo sharing behavior.