17,568 research outputs found
High-Level Concepts for Affective Understanding of Images
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 black-box, 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
Multilabel Classification with R Package mlr
We implemented several multilabel classification algorithms in the machine
learning package mlr. The implemented methods are binary relevance, classifier
chains, nested stacking, dependent binary relevance and stacking, which can be
used with any base learner that is accessible in mlr. Moreover, there is access
to the multilabel classification versions of randomForestSRC and rFerns. All
these methods can be easily compared by different implemented multilabel
performance measures and resampling methods in the standardized mlr framework.
In a benchmark experiment with several multilabel datasets, the performance of
the different methods is evaluated.Comment: 18 pages, 2 figures, to be published in R Journal; reference
correcte
Affective Image Content Analysis: Two Decades Review and New Perspectives
Images can convey rich semantics and induce various emotions in viewers.
Recently, with the rapid advancement of emotional intelligence and the
explosive growth of visual data, extensive research efforts have been dedicated
to affective image content analysis (AICA). In this survey, we will
comprehensively review the development of AICA in the recent two decades,
especially focusing on the state-of-the-art methods with respect to three main
challenges -- the affective gap, perception subjectivity, and label noise and
absence. We begin with an introduction to the key emotion representation models
that have been widely employed in AICA and description of available datasets
for performing evaluation with quantitative comparison of label noise and
dataset bias. We then summarize and compare the representative approaches on
(1) emotion feature extraction, including both handcrafted and deep features,
(2) learning methods on dominant emotion recognition, personalized emotion
prediction, emotion distribution learning, and learning from noisy data or few
labels, and (3) AICA based applications. Finally, we discuss some challenges
and promising research directions in the future, such as image content and
context understanding, group emotion clustering, and viewer-image interaction.Comment: Accepted by IEEE TPAM
- …