As sharing personal media online becomes easier and widely spread, new privacy concerns emerge - especially when the persistent nature of the media and associated context reveals details about the physical and social context in which the media items were created. In a first-of-its-kind study, we use context-aware camerephone devices to examine privacy decisions in mobile and online photo sharing. Through data analysis on a corpus of privacy decisions and associated context data from a real-world system, we identify relationships between location of photo capture and photo privacy settings. Our data analysis leads to further questions which we investigate through a set of interviews with 15 users. The interviews reveal common themes in privacy considerations: security, social disclosure, identity and convenience. Finally, we highlight several implications and opportunities for design of media sharing applications, including using past privacy patterns to prevent oversights and errors.

What happens when you can access all the world's media, but the access is constrained by screen size, bandwidth, attention, and battery life? We present a novel mobile context-aware software prototype that enables access to images on the go. Our prototype utilizes the channel metaphor to give users contextual access to media of interest according to key dimensions: spatial, social, and topical.

The availability of map interfaces and location-aware devices makes a growing amount of unstructured, geo-referenced information available on the Web. In particular, over twelve million geo-referenced photos are now available on Flickr, a popular photo-sharing website. We show a method to analyze the Flickr data and generate aggregate knowledge in the form of "representative tags" for arbitrary areas in the world. We display these tags on a map interface in an interactive web application along with images associated with each tag. We then use the implicit feedback of the aggregate user interactions with the tags and images to learn which images best describe the area shown on the map.

Though cameraphones are rapidly becoming the dominant platform for consumer digital photography, users still face difficulties in transferring, managing, and sharing photos captured with cameraphones. The Mobile Media Metadata 2 (MMM2) system removes the difficulty in transferring photos from the device by providing an automatic upload capability and uses metadata about the context in which a photo was captured to simplify photo management and streamline the sharing process. In our MMM2 system, we have leveraged collaborative filtering techniques to infer the likely sharing recipients for photos based on contextual metadata, which allows the system to accurately guess likely share recipients for a photo and present them to the photographer at the time of capture.

The number of people using cameraphones is growing by tens of millions every month. Yet the majority of cameraphone users have difficulty transferring photos off their phone and sharing them with others. PhotoRouter is a software application for cameraphones that makes the photo sharing process destination-centric by allowing users to focus on who the photo should go to, not how it needs to get there. Attempting to produce an application which meets user needs better than current, technology-centric cameraphone photo sharing applications, we designed PhotoRouter. In this paper we describe PhotoRouter's user interface innovations that we will show in our technical demonstration.

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.

In this paper, we focus on the use of context-aware, collaborative filtering, machine-learning techniques that leverage automatically sensed and inferred contextual metadata together with computer vision analysis of image content to make accurate predictions about the human subjects depicted in cameraphone photos. We apply Sparse-Factor Analysis (SFA) to both the contextual metadata gathered in the MMM2 system and the results of PCA (Principal Components Analysis) of the photo content to achieve a 60% face recognition accuracy of people depicted in our cameraphone photos, which is 40% better than media analysis alone. In short, we use context-aware media analysis to solve the face recognition problem for cameraphone photos.

The recent popularity of mobile camera phones allows for new opportunities to gather important metadata at the point of capture. This paper describes a method for generating metadata for photos using spatial, temporal, and social context. We describe a system we implemented for inferring location information for pictures taken with camera phones and its performance evaluation. We propose that leveraging contextual metadata at the point of capture can address the problems of the semantic and sensory gaps. In particular, combining and sharing spatial, temporal, and social contextual metadata from a given user and across users allows us to make inferences about media content.