Affective computing
Semantics in Visual Information Retrieval
IEEE MultiMedia
K-DIME: An Affective Image Filtering System
IEEE MultiMedia
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
CEDD: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Affective image classification using features inspired by psychology and art theory
Proceedings of the international conference on Multimedia
Analyzing and predicting sentiment of images on the social web
Proceedings of the international conference on Multimedia
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Emotion-Based textile indexing using colors and texture
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Object retrieval by query with sensibility based on the KANSEI-Vocabulary scale
ECCV'06 Proceedings of the 2006 international conference on Computer Vision in Human-Computer Interaction
The MPEG-7 visual standard for content description-an overview
IEEE Transactions on Circuits and Systems for Video Technology
Detect'11: international workshop on DETecting and Exploiting Cultural diversiTy on the social web
Proceedings of the 20th ACM international conference on Information and knowledge management
Proceedings of the 20th ACM international conference on Multimedia
Hi-index | 0.00 |
Images convey opinions and emotional messages in the communication process. With the increasing use of images in various scenarios, the area of opinion mining and sentiment analysis has recently received a huge burst of interest. In particular, in the context of social web the ability of identify different emotions in images might help providing diversification of results, thus proposing different viewpoints to users. In this paper we analyze which are the features (e.g., colors, texture) that are more strictly related to the emotional content of a picture, thus allowing a classification connected with the emotion conveyed by images. We present the results on a set of natural images in order to reduce as much as possible the interaction with content semantics.