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Digital portrait photographs are everywhere, and while the number of face pictures keeps growing, not much work has been done to on automatic portrait beauty assessment. In this paper, we design a specific framework to automatically evaluate the beauty of digital portraits. To this end, we procure a large dataset of face images annotated not only with(More)
In this paper we describe a system that automatically extracts appealing scenes from a set of broadcasting videos. Unlike traditional computational aesthetic models that try to predict the hardly measurable degree of "beauty", we chose to build a system that retrieves "interesting" scenes. We create a training database of Flickr images annotated with their(More)
The IRIM group is a consortium of French teams working on Multimedia Indexing and Retrieval. This paper describes its participation to the TRECVID 2011 semantic indexing and instance search tasks. For the semantic indexing task, our approach uses a six-stages processing pipelines for computing scores for the likelihood of a video shot to contain a target(More)
To choose restaurants and coffee shops, people are increasingly relying on social-networking sites. In a popular site such as Foursquare or Yelp, a place comes with descriptions and reviews, and with profile pictures of people who frequent them. Descriptions and reviews have been widely explored in the research area of data mining. By contrast, profile(More)
This year EURECOM participated in the TRECVID light Semantic Indexing (SIN) Task for the submission of four different runs for 50 concepts. Our submission builds on the runs submitted last year at the 2010 SIN task by adding more effective visual features to the third system built last year, the details of which can be found in [10]. Two of our systems(More)
Thumbnails play such an important role in online videos. As the most representative snapshot, they capture the essence of a video and provide the first impression to the viewers; ultimately, a great thumbnail makes a video more attractive to click and watch. We present an automatic thumbnail selection system that exploits two important characteristics(More)
In this paper we present Saliency Moments, a new, holistic descriptor for image recognition inspired by two biological vision principles: the <i>gist</i> perception and the selective visual attention. While traditional image features extract either local or global discriminative properties from the visual content, we use a hybrid approach that exploits some(More)
The notion of creativity, as opposed to related concepts such as beauty or interestingness, has not been studied from the perspective of automatic analysis of multimedia content. Meanwhile, short online videos shared on social media platforms, or micro-videos, have arisen as a new medium for creative expression. In this paper we study creative micro-videos(More)
The IRIM group is a consortium of French teams working on Multimedia Indexing and Retrieval. This paper describes its participation to the TRECVID 2012 semantic indexing and instance search tasks. For the semantic indexing task, our approach uses a six-stages processing pipelines for computing scores for the likelihood of a video shot to contain a target(More)
The dynamics of attention in social media tend to obey power laws. Attention concentrates on a relatively small number of popular items and neglecting the vast majority of content produced by the crowd. Although popularity can be an indication of the perceived value of an item within its community, previous research has hinted to the fact that popularity is(More)