Sharing, privacy and trust issues for photo collections

Digital libraries are quickly being adopted by the masses. Technological developments now allow community groups, clubs, and even ordinary individuals to create their own, publicly accessible collections. However, users may not be fully aware of the potential privacy implications of submitting their documents to a digital library, and may hold misconceptions of the technological support for preserving their privacy. We present results from 18 autoethnographic investigations and 19 observations / interviews into privacy issues that arise when people make their personal photo collections available online. The Adams' privacy model is used to discuss the findings according to information receiver, information sensitivity, and information usage. Further issues of trust and ad hoc poorly supported protection strategies are presented. Ultimately while photographic data is potentially highly sensitive, the privacy risks are often hidden and the protection mechanisms are limited

Mining user activity as a context source for search and retrieval

Nowadays in information retrieval it is generally accepted that if we can better understand the context of users then this could help the search process, either at indexing time by including more metadata or at retrieval time by better modelling the user context. In this work we explore how activity recognition from tri-axial accelerometers can be employed to model a user's activity as a means of enabling context-aware information retrieval. In this paper we discuss how we can gather user activity automatically as a context source from a wearable mobile device and we evaluate the accuracy of our proposed user activity recognition algorithm. Our technique can recognise four kinds of activities which can be used to model part of an individual's current context. We discuss promising experimental results, possible approaches to improve our algorithms, and the impact of this work in modelling user context toward enhanced search and retrieval

Seeing Behind the Camera: Identifying the Authorship of a Photograph

We introduce the novel problem of identifying the photographer behind a photograph. To explore the feasibility of current computer vision techniques to address this problem, we created a new dataset of over 180,000 images taken by 41 well-known photographers. Using this dataset, we examined the effectiveness of a variety of features (low and high-level, including CNN features) at identifying the photographer. We also trained a new deep convolutional neural network for this task. Our results show that high-level features greatly outperform low-level features. We provide qualitative results using these learned models that give insight into our method's ability to distinguish between photographers, and allow us to draw interesting conclusions about what specific photographers shoot. We also demonstrate two applications of our method.Comment: Dataset downloadable at http://www.cs.pitt.edu/~chris/photographer To Appear in CVPR 201

Flickr: A case study of Web2.0

The “photosharing” site Flickr is one of the most commonly cited examples used to define Web2.0. This paper explores where Flickr’s real novelty lies, examining its functionality and its place in the world of amateur photography. The paper draws on a wide range of sources including published interviews with its developers, user opinions expressed in forums, telephone interviews and content analysis of user profiles and activity. Flickr’s development path passes from an innovative social game to a relatively familiar model of a website, itself developed through intense user participation but later stabilising with the reassertion of a commercial relationship to the membership. The broader context of the impact of Flickr is examined by looking at the institutions of amateur photography and particularly the code of pictorialism promoted by the clubs and industry during the C20th. The nature of Flickr as a benign space is premised on the way the democratic potential of photography is controlled by such institutions. Several optimistic views of the impact of Flickr such as its facilitation of citizen journalism, “vernacular creativity” and in learning as an “affinity space” are evaluated. The limits of these claims are identified in the way that the system is designed to satisfy commercial purposes, continuing digital divides in access and the low interactivity and criticality on Flickr. Flickr is an interesting source of change, but can only to be understood in the perspective of long term development of the hobby and wider social processes

Personal life event detection from social media

Creating video clips out of personal content from social media is on the rise. MuseumOfMe, Facebook Lookback, and Google Awesome are some popular examples. One core challenge to the creation of such life summaries is the identification of personal events, and their time frame. Such videos can greatly benefit from automatically distinguishing between social media content that is about someone's own wedding from that week, to an old wedding, or to that of a friend. In this paper, we describe our approach for identifying a number of common personal life events from social media content (in this paper we have used Twitter for our test), using multiple feature-based classifiers. Results show that combination of linguistic and social interaction features increases overall classification accuracy of most of the events while some events are relatively more difficult than others (e.g. new born with mean precision of .6 from all three models)

Smartphone picture organization: a hierarchical approach

We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization.Peer ReviewedPreprin