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Every culture and language is unique. Our work expressly focuses on the uniqueness of culture and language in relation to human affect, specifically sentiment and emotion semantics, and how they manifest in social multimedia. We develop sets of sentiment- and emotion-polarized visual concepts by adapting semantic structures called adjective-noun pairs,(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)
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)
— 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)
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)
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)
Bag of Words (BOW) models are nowadays one of the most effective methods for visual categorization. They use visual dictionaries to aggregate the set of local descriptors extracted from a given image. Despite their high discriminative ability, one of the major drawbacks of BOW still remains the computational cost of the visual dictionary, built by(More)
1 Abstract 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(More)