Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affective image classification using features inspired by psychology and art theory
Proceedings of the international conference on Multimedia
Vlfeat: an open and portable library of computer vision algorithms
Proceedings of the international conference on Multimedia
An eye fixation database for saliency detection in images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Evaluating the visual quality of web pages using a computational aesthetic approach
Proceedings of the fourth ACM international conference on Web search and data mining
Computer Vision: Algorithms and Applications
Computer Vision: Algorithms and Applications
Visual Object Recognition
Emotion based classification of natural images
Proceedings of the 2011 international workshop on DETecting and Exploiting Cultural diversiTy on the social web
MM '11 Proceedings of the 19th ACM international conference on Multimedia
The Visual Extent of an Object
International Journal of Computer Vision
What makes an image memorable?
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Real-Time Visual Concept Classification
IEEE Transactions on Multimedia
Assessing the aesthetic quality of photographs using generic image descriptors
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Sentribute: image sentiment analysis from a mid-level perspective
Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining
Unveiling the multimedia unconscious: implicit cognitive processes and multimedia content analysis
Proceedings of the 21st ACM international conference on Multimedia
Large-scale visual sentiment ontology and detectors using adjective noun pairs
Proceedings of the 21st ACM international conference on Multimedia
Stochastic bottom-up fixation prediction and saccade generation
Image and Vision Computing
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Most artworks are explicitly created to evoke a strong emotional response. During the centuries there were several art movements which employed different techniques to achieve emotional expressions conveyed by artworks. Yet people were always consistently able to read the emotional messages even from the most abstract paintings. Can a machine learn what makes an artwork emotional? In this work, we consider a set of 500 abstract paintings from Museum of Modern and Contemporary Art of Trento and Rovereto (MART), where each painting was scored as carrying a positive or negative response on a Likert scale of 1-7. We employ a state-of-the-art recognition system to learn which statistical patterns are associated with positive and negative emotions. Additionally, we dissect the classification machinery to determine which parts of an image evokes what emotions. This opens new opportunities to research why a specific painting is perceived as emotional. We also demonstrate how quantification of evidence for positive and negative emotions can be used to predict the way in which people observe paintings.