Machine Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
WND-CHARM: Multi-purpose image classification using compound image transforms
Pattern Recognition Letters
Impressionism, expressionism, surrealism: Automated recognition of painters and schools of art
ACM Transactions on Applied Perception (TAP)
Emotion related structures in large image databases
Proceedings of the ACM International Conference on Image and Video Retrieval
Affective image classification using features inspired by psychology and art theory
Proceedings of the international conference on Multimedia
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Gaze- and speech-enhanced content-based image retrieval in image tagging
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Content-Based affective image classification and retrieval using support vector machines
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Studying aesthetics in photographic images using a computational approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
XCSF for prediction on emotion induced by image based on dimensional theory of emotion
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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In this work, we study people's emotions evoked by viewing abstract art images based on traditional low-level image features within a binary classification framework. Abstract art is used here instead of artistic or photographic images because those contain contextual information that influences the emotional assessment in a highly individual manner. Whether an image of a cat or a mountain elicits a negative or positive response is subjective. After discussing challenges concerning image emotional semantics research, we empirically demonstrate that the emotions triggered by viewing abstract art images can be predicted with reasonable accuracy by machine using a variety of low-level image descriptors such as color, shape, and texture. The abstract art dataset that we created for this work has been made downloadable to the public.