Paper Info
Title
High-Level Concepts for Affective Understanding of Images
Abstract
This paper aims to bridge the affective gap between image content and the emotional response of the viewer it elicits by using High-Level Concepts (HLCs). In contrast to previous work that relied solely on low-level features or used convolutional neural network (CNN) as a blackbox, we use HLCs generated by pretrained CNNs in an explicit way to investigate the relations/associations between these HLCs and a (small) set of Ekman's emotional classes. As a proof-of-concept, we first propose a linear admixture model for modeling these relations, and the resulting computational framework allows us to determine the associations between each emotion class and certain HLCs (objects and places). This linear model is further extended to a nonlinear model using support vector regression (SVR) that aims to predict the viewer's emotional response using both low-level image features and HLCs extracted from images. These class-specific regressors are then assembled into a regressor ensemble that provide a flexible and effective predictor for predicting viewer's emotional responses from images. Experimental results have demonstrated that our results are comparable to existing methods, with a clear view of the association between HLCs and emotional classes that is ostensibly missing in most existing work.
Year
DOI
Venue
2017
10.1109/WACV.2017.81
2017 IEEE Winter Conference on Applications of Computer Vision (WACV)
Keywords
DocType
Volume
high-level concepts,affective image understanding,image content,viewer emotional response,HLC,emotional classes,linear admixture model,nonlinear model,support vector regression,SVR,low-level image features,class-specific regressors,regressor ensemble
Conference
abs/1705.02751
ISSN
ISBN
Citations 
2472-6737
978-1-5090-4823-6
2
PageRank 
References 
Authors
0.36
16
4
Name
Order
Citations
PageRank
Afsheen Rafaqat Ali120.36
Usman Shahid220.36
Mohsen Ali3388.40
Jeffrey Ho42190101.78