Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantics in Visual Information Retrieval
IEEE MultiMedia
Affective image classification using features inspired by psychology and art theory
Proceedings of the international conference on Multimedia
Web Horror Image Recognition Based on Context-Aware Multi-instance Learning
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Context-aware affective images classification based on bilayer sparse representation
Proceedings of the 20th ACM international conference on Multimedia
Sentribute: image sentiment analysis from a mid-level perspective
Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining
Large-scale visual sentiment ontology and detectors using adjective noun pairs
Proceedings of the 21st ACM international conference on Multimedia
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Automatic image emotion analysis has emerged as a hot topic due to its potential application on high-level image understanding. Considering the fact that the emotion evoked by an image is not only from its global appearance but also interplays among local regions, we propose a novel affective image classification system based on bilayer sparse representation (BSR). The BSR model contains two layers: The global sparse representation (GSR) is to define global similarities between a test image and all the training images; and the local sparse representation (LSR) is to define similarities of local regions' appearances and their co-occurrence between a test image and all the training images. The experiments on real data sets demonstrate that our system is effective on image emotion recognition.