Recent studies have utilizes color, texture, and composition information of images to
achieve affective image classification. However, the features related to spatial-frequency
domain that were proven to be useful for traditional pattern recognition have not been
tested in this field yet. Furthermore, the experiments conducted by previous studies are
not internationally-comparable due to the experimental paradigm adopted. In addition,
contributed by recent advances in methodology, that are, Hilbert-Huang Transform (HHT)
(i.e. Empirical Mode Decomposition (EMD) and Hilbert Transform (HT)), the resolution
of frequency analysis has been improved. Hence, the goal of this research is to achieve
the affective image-classification task by adopting a standard experimental paradigm introduces
by psychologists in order to produce international-comparable and reproducible
results; and also to explore the affective hidden patterns of images in the spatial-frequency
domain. To accomplish these goals, multiple human-subject experiments were conducted
in laboratory. Extended Classifier Systems (XCSs) was used for model building because
the XCS has been applied to a wide range of classification tasks and proved to be competitive
in pattern recognition. To exploit the information in the spatial-frequency domain,
the traditional EMD has been extended to a two-dimensional version. To summarize, the
model built by using the XCS achieves Area Under Curve (AUC) = 0.91 and accuracy rate
over 86%. The result of the XCS was compared with other traditional machine-learning
algorithms (e.g., Radial-Basis Function Network (RBF Network)) that are normally used
for classification tasks. Contributed by proper selection of features for model building,
user-independent findings were obtained. For example, it is found that the horizontal visual
stimulations contribute more to the emotion elicitation than the vertical visual stimulation.
The effect of hue, saturation, and brightness; is also presented.
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