Semantics in Visual Information Retrieval
IEEE MultiMedia
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
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
Studying aesthetics in photographic images using a computational approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Scaring or pleasing: exploit emotional impact of an image
Proceedings of the 20th ACM international conference on Multimedia
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In image understanding, the automatic recognition of emotion in an image is becoming important from an applicative viewpoint. 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 context-aware classification model based on bilayer sparse representation (BSR) that simultaneously takes the local context and global-local context into account. The BSR model contains two layers: global sparse representation (GSR) and local sparse representation (LSR). The GSR is to define global similarities between a test image and all training images; while the LSR is to define similarities of local regions' appearances and their co-occurrence between a test image and all training images. The experiments on two data sets demonstrate that our method is effective on affective images classification.