A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
ACSC '04 Proceedings of the 27th Australasian conference on Computer science - Volume 26
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Inverted files for text search engines
ACM Computing Surveys (CSUR)
AnnoSearch: Image Auto-Annotation by Search
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large-Scale Discovery of Spatially Related Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Corpus-based semantic class mining: distributional vs. pattern-based approaches
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Partition min-hash for partial duplicate image discovery
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Visual and semantic similarity in ImageNet
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Hi-index | 0.00 |
We investigated how shape features in natural images influence emotions aroused in human beings. Shapes and their characteristics such as roundness, angularity, simplicity, and complexity have been postulated to affect the emotional responses of human beings in the field of visual arts and psychology. However, no prior research has modeled the dimensionality of emotions aroused by roundness and angularity. Our contributions include an in depth statistical analysis to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset we provide evidence for the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. We combine our shape features with other state-of-the-art features to show a gain in prediction and classification accuracy. We model emotions from a dimensional perspective in order to predict valence and arousal ratings which have advantages over modeling the traditional discrete emotional categories. Finally, we distinguish images with strong emotional content from emotionally neutral images with high accuracy.