A study on video browsing strategies
A study on video browsing strategies
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Design of High-Level Features for Photo Quality Assessment
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Real-Time Computerized Annotation of Pictures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Automatic textile image annotation by predicting emotional concepts from visual features
Image and Vision Computing
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Studying aesthetics in photographic images using a computational approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Can we understand van gogh's mood?: learning to infer affects from images in social networks
Proceedings of the 20th ACM international conference on Multimedia
Affective image adjustment with a single word
The Visual Computer: International Journal of Computer Graphics
| Hi-index | 0.00 |
With increasing the importance of affective computing, it becomes necessary to retrieve and process images according to human affects or preference. However, judging such affective qualities of images is a highly subjective task. In spite of the lack of firm rules, certain features in images are believed to be more related than certain others. In this paper, we suggest predicting certain affective features include in an image using color composition that constitutes the scene. Using such a feature is inspired from Kobayashi's color scale that studies the relation between colors/color compositions and human's affects. Thus, we propose a Probabilistic Affective Model (PAM) to estimate the probabilities that an image is related to certain affective features. For this, we segment an image using mean-shift clustering algorithm, and extract more important regions, which are called seed regions, based on their properties. Thereafter, we find the dominant color compositions among those seed regions and their neighboring regions. Finally, from such color compositions, we infer the numerical ratings for some affective features. To assess the effectiveness of our PAM, we compared its results with 52 users' affective judgments. It was tested with online photo images, then the results show our PAM produced the recall of 85.22% and the precision of 78.16% on average. Potential applications include content-based image retrieval and design of web page interfaces.