Identifying Emotions Aroused from Paintings
Understanding the emotional appeal of paintings is a significant research problem related to affective image classification. The problem is challenging in part due to the scarceness of manually-classified paintings. Our work proposes to apply statistical
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Adobe Systems Inc., Mountain View, USA [email protected] 2 Amazon.com, Inc., Seattle, USA [email protected] The Pennsylvania State University, State College, USA {mgn1,radams,jwang,jiali}@psu.edu
Abstract. Understanding the emotional appeal of paintings is a significant research problem related to affective image classification. The problem is challenging in part due to the scarceness of manually-classified paintings. Our work proposes to apply statistical models trained over photographs to infer the emotional appeal of paintings. Directly applying the learned models on photographs to paintings cannot provide accurate classification results, because visual features extracted from paintings and natural photographs have different characteristics. This work presents an adaptive learning algorithm that leverages labeled photographs and unlabeled paintings to infer the visual appeal of paintings. In particular, we iteratively adapt the feature distribution in photographs to fit paintings and maximize the joint likelihood of labeled and unlabeled data. We evaluate our approach through two emotional classification tasks: distinguishing positive from negative emotions, and differentiating reactive emotions from non-reactive ones. Experimental results show the potential of our approach. Keywords: Classification tograph · Visual art
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· Evoked emotion · Adaptive learning · Pho-
Introduction
Visual artworks such as paintings can evoke a variety of emotional responses from human observers, such as calmness, dynamism, turmoil, and happiness. Automatic inference of the emotions aroused from a given painting is an important research question due to its potential application in large-scale image management and human perception understanding. For instance, the affective capability of paintings might be leveraged to determine which artwork might be used to decorate workplaces, hospitals, gymnasia, and schools. The problem is highly This material is based upon work supported by the National Science Foundation under Grant No. 1110970. The work was done when X. Lu and N. Sawant were with Penn State University. c Springer International Publishing Switzerland 2016 G. Hua and H. J´ egou (Eds.): ECCV 2016 Workshops, Part I, LNCS 9913, pp. 48–63, 2016. DOI: 10.1007/978-3-319-46604-0 4
Identifying Emotions Aroused from Paintings
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challenging because many paintings are abstract in nature. The exact association between visual features and evoked emotions is often not obvious. An applicable framework that has been used to quantify general emotion recognition problem from color photographs [3,8,13,14] is to learn a statistical model that connects handcrafted visual features extracted from the training images with their associated emotional labels. However, unlike emotion recognition in photographs which can leverage existing annotated datasets such as the International Affective Picture System (IAPS) [10], we do not have a validated dataset with sufficient manually-labeled paintings. Previous methods [7,11,12] conducted training on a small c
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