Predicting Image Emotion Distribution by Learning Labels’ Correlation

Fan, Yangyu; Yang, Hansen; Li, Zuhe; Liu, Shu

doi:10.1109/access.2019.2939681

Cited by 8 publications

(4 citation statements)

References 30 publications

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“…Literature [28] combined the visual attention mechanism guided by the saliency map with the CNN architecture to achieve better sentiment classification performance based on natural images. In order to maximize the extraction of features that can represent image emotions, literature [29] proposed a cropping method that uses a fully convolutional network to select emotional regions from an image and uses the interdependence of tags to construct a structured learning model. Aiming at the local image sentiment classification, literature [30] used the feature pyramid network to extract multilayer depth features to remove redundant nonemotional areas.…”

Section: The Related Methods Based On Image Sentiment Analysismentioning

confidence: 99%

Sentiment Analysis of Chinese Paintings Based on Lightweight Convolutional Neural Network

Bian

Shen

2021

Wireless Communications and Mobile Computing

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Chinese painting is one of the representatives of our country’s outstanding traditional culture, and it embodies the long history and intellectual wisdom of the Chinese nation. In the paper, we combine the artistic characteristics of Chinese paintings and use an optimized SqueezeNet model to study the sentiment analysis of Chinese paintings. To make full use of the advantages of lightweight convolutional neural networks, we make two optimizations based on SqueezeNet. On the one hand, expand the model width to obtain more effective Chinese painting sentiment features for classification tasks, thereby improving the classification accuracy of the model. On the other hand, introduce the idea of residual network to prevent gradient disappearance and gradient explosion in the training process, thereby enhancing the model’s generalization ability. To verify the effectiveness of the optimized SqueezeNet model used in the sentiment analysis of Chinese paintings, four kinds of sentiment classifications were carried out on the multitheme Chinese paintings downloaded on the Internet. The results of comparative experiments show that the optimized SqueezeNet model used in this paper can improve the accuracy of classification and has better generalization ability. Finally, the research results of this paper can be applied to the protection of traditional culture, the appreciation of traditional Chinese painting, and art education and training, which is conducive to the inheritance and innovation of the national quintessence and promotes the prosperity and development of traditional art and culture.

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Section: The Related Methods Based On Image Sentiment Analysismentioning

confidence: 99%

Sentiment Analysis of Chinese Paintings Based on Lightweight Convolutional Neural Network

Bian

Shen

2021

Wireless Communications and Mobile Computing

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“…It is efficient in identification and segmentation, with a straightforward and rapid calculation method. As a result, it has been applied in many fields [29][30][31]…”

Section: Clark Distance Measurementioning

confidence: 99%

A novel Clark distance measure method for intuitionistic fuzzy sets with pattern classification applications

WU,

Wang

2024

Preprint

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Fuzzy sets possess remarkable abilities in expressing and handling information uncertainty, which has resulted in their widespread application in various fields. Nevertheless, distance measurement between IFSs for quantitating their differences and levels of differentiation has remained an open problem that deserves attention. Despite the development of various metrics, they either lack intuitive insight or do not satisfy the axioms of distance measurement, leading to counterintuitive results. To address these issues, this paper proposed a distance measurement method based on Clark divergence, which satisfies the distance measurement axioms and exhibits nonlinearity. Numerical examples demonstrate that our method effectively distinguishes different indicators, yielding more reasonable results. Moreover, when comparing relative differences of the results, our method demonstrated superior adaptability to complex environmental decision-making, providing decision-makers with more accurate and confidential judgments. The pattern classification algorithm designed in this paper will offer a promising solution to inference problems.

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“…Such information is important for understanding fine-grained emotions, especially when ambiguities exist [7]. In recent years, many effective EDL methods have been proposed [13][14][15][16][17][18][19][20]. However, one of the major problems for the development of EDL models is the lack of emotion distribution in annotated datasets, due to the difficulty to annotate emotion distributions.…”

Section: Emotion Distribution Learningmentioning

confidence: 99%

“…Xiong et al [17] proposed an EDL model based on convolutional neural networks that utilizes the polarity and the sparsity of emotion labels. Fan et al [18] designed an EDL method to predict image emotion distribution by learning labels' correlation. Xi et al [19] proposed emotion distribution learning based on surface electromyography for predicting the intensities of basic emotions.…”

Section: Introductionmentioning

confidence: 99%

Emotion Label Enhancement via Emotion Wheel and Lexicon

Zeng

Chen

et al. 2021

Mathematical Problems in Engineering

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Emotion Distribution Learning (EDL) is a recently proposed multiemotion analysis paradigm, which identifies basic emotions with different degrees of expression in a sentence. Different from traditional methods, EDL quantitatively models the expression degree of the corresponding emotion on the given instance in an emotion distribution. However, emotion labels are crisp in most existing emotion datasets. To utilize traditional emotion datasets in EDL, label enhancement aims to convert logical emotion labels into emotion distributions. This paper proposed a novel label enhancement method, called Emotion Wheel and Lexicon-based emotion distribution Label Enhancement (EWLLE), utilizing the affective words’ linguistic emotional information and the psychological knowledge of Plutchik’s emotion wheel. The EWLLE method generates separate discrete Gaussian distributions for the emotion label of sentence and the emotion labels of sentiment words based on the psychological emotion distance and combines the two types of information into a unified emotion distribution by superposition of the distributions. The extensive experiments on 4 commonly used text emotion datasets showed that the proposed EWLLE method has a distinct advantage over the existing EDL label enhancement methods in the emotion classification task.

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Predicting Image Emotion Distribution by Learning Labels’ Correlation

Cited by 8 publications

References 30 publications

Sentiment Analysis of Chinese Paintings Based on Lightweight Convolutional Neural Network

Sentiment Analysis of Chinese Paintings Based on Lightweight Convolutional Neural Network

A novel Clark distance measure method for intuitionistic fuzzy sets with pattern classification applications

Emotion Label Enhancement via Emotion Wheel and Lexicon

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