The nature of statistical learning theory
The nature of statistical learning theory
Coding, Analysis, Interpretation, and Recognition of Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affective computing
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantics in Visual Information Retrieval
IEEE MultiMedia
Tatsuo Tsuji: Multimedia Data Access Based on the Sensitivity Factors
IDEAS '00 Proceedings of the 2000 International Symposium on Database Engineering & Applications
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Automatic Spoken Affect Classification and Analysis
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Affective image classification using features inspired by psychology and art theory
Proceedings of the international conference on Multimedia
Using association rules to discover color-emotion relationships based on social tagging
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Towards multi-semantic image annotation with graph regularized exclusive group lasso
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Analyzing emotional semantics of abstract art using low-level image features
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
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 paper a new method to classify and retrieve affective images is proposed. First users express the affective semantics of the images with adjective words; process the data got by Semantic Differential method to obtain main factors of affection and establish affective space; extract low-level visual features of image to construct visual feature space; calculate the correlation between affective space and visual feature space with SVMs. The prototype system that embodies trained SVMs has been implemented. The system can classify the images automatically and support the affective image retrieval. The experimental results prove the effectiveness of this method.