Analyzing Emotional Semantics of Abstract Art Using Low-Level Image Features

He, Zhongshi; Augilius, Eimontas; Honkela, Timo; Laaksonen, Jorma; Gamper, Hannes; Alene, Henok

doi:10.1007/978-3-642-24800-9_38

Cited by 26 publications

(15 citation statements)

References 15 publications

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“…On the path of research in affective image classification [19][20][21][22][23], the achieved accuracy rates are relatively low. Furthermore, the use of definition in "discrete" emotions also caused these experiments hard to reproduce in countries other than the United States.…”

Section: Methods and Materials 21 Emotion Theoriesmentioning

confidence: 99%

Applying LCS To Affective Image Classification In Spatial-Frequency Domain

Lee

Hsiao

2014

Journal of Artificial Intelligence and Soft Computing Research

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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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Section: Methods and Materials 21 Emotion Theoriesmentioning

confidence: 99%

Applying LCS To Affective Image Classification In Spatial-Frequency Domain

Lee

Hsiao

2014

Journal of Artificial Intelligence and Soft Computing Research

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“…Affective concepts were modeled using color palettes, which showed that the bag of colors and Fisher vectors (i.e., higher order statistics about the distribution of local descriptors) were effective [9]. Zhang et al [27] characterized shape through Zernike features, edge statistics features, object statistics, and Gabor filters. Emotion-histogram and bag-of-emotion features were used to classify emotions by Solli et al [24].…”

Section: Related Workmentioning

confidence: 99%

“…Researchers [9,11,18,23,25,26] investigated factors such as color, texture, composition, and simple semantics to understand emotions, but have not quantitatively addressed the effect of perceptual shapes. The study that did explore shapes by Zhang et al [27] predicted emotions evoked by viewing abstract art images through low-level features like color, shape, and texture. However, this work only handles abstract images, and focused on the representation of textures with little accountability of shape.…”

Section: Introductionmentioning

confidence: 99%

On shape and the computability of emotions

Suryanarayan

Adams

et al. 2012

Proceedings of the 20th ACM International Conference on Multimedia

137

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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 simplicitycomplexity on predicting emotional content in images. We combine our shape features with other state-of-theart 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.

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“…We select three kinds of cross-domain databases such as Abstract100, Abstract 280, and STL-10 database [15,16].…”

Section: Local Feature Extraction In Transfer Learningmentioning

confidence: 99%

“…And the F1-Measurement represents the harmonic mean between the index of Precision and Recall. In this experiment, we adopt each index as shown in Formula (16)(17)(18)(19).…”

Section: Two Kinds Of Target Object Classificationmentioning

confidence: 99%

Classification methods of a small sample target object in the sky based on the higher layer visualizing feature and transfer learning deep networks

Chen

Meng

Wen

et al. 2018

J Wireless Com Network

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The effective classification methods of the small target objects in the no-fly zone are of great significance to ensure safety in the no-fly zone. But, due to the differences of the color and texture for the small target objects in the sky, this may be unobvious, such as the birds, unmanned aerial vehicles (UAVs), and kites. In this paper, we introduced the higher layer visualizing feature extraction method based on the hybrid deep network model to obtain the higher layer feature through combining the Sparse Autoencoder (SAE) model, the Convolutional Neural Network (CNN) model, and the regression classifier model to classify the different types of the target object images. In addition, because the sample numbers of the small sample target objects in the sky may be not sufficient, we cannot obtain much more local features directly to realize the classification of the target objects based on the higher layer visualizing feature extraction; we introduced the transfer learning in the SAE model to gain the cross-domain higher layer local visualizing features and sent the cross-domain higher layer local visualizing features and the images of the target-domain small sample object images into the CNN model, to acquire the global visualizing features of the target objects. Experimental results have shown that the higher layer visualizing feature extraction and the transfer learning deep networks are effective for the classification of small sample target objects in the sky.

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Analyzing Emotional Semantics of Abstract Art Using Low-Level Image Features

Cited by 26 publications

References 15 publications

Applying LCS To Affective Image Classification In Spatial-Frequency Domain

Applying LCS To Affective Image Classification In Spatial-Frequency Domain

On shape and the computability of emotions

Classification methods of a small sample target object in the sky based on the higher layer visualizing feature and transfer learning deep networks

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