Affective image classification using features inspired by psychology and art theory
Proceedings of the international conference on Multimedia
Saliency moments for image categorization
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Studying aesthetics in photographic images using a computational approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A multimedia retrieval framework based on automatic graded relevance judgments
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Conceptualizing Birkhoff's aesthetic measure using Shannon entropy and Kolmogorov complexity
Computational Aesthetics'07 Proceedings of the Third Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
Semantic indexing and computational aesthetics: interactions, bridgesand boundaries
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Beauty is here: evaluating aesthetics in videos using multimodal features and free training data
Proceedings of the 21st ACM international conference on Multimedia
Rare is interesting: connecting spatio-temporal behavior patterns with subjective image appeal
Proceedings of the 2nd ACM international workshop on Geotagging and its applications in multimedia
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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 corresponding Flickr "interestingness" degree. We then extract existing and novel aesthetic/semantic features from the training set. Based on such features, we build a graded-relevance "interestingness" model and we rank the test shots according to their predicted "interestingness".