Prediction of emotion distribution of images based on weighted K-nearest neighbor-attention mechanism

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Kai Cheng
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引用次数: 0

Abstract

Existing methods for classifying image emotions often overlook the subjective impact emotions evoke in observers, focusing primarily on emotion categories. However, this approach falls short in meeting practical needs as it neglects the nuanced emotional responses captured within an image. This study proposes a novel approach employing the weighted closest neighbor algorithm to predict the discrete distribution of emotion in abstract paintings. Initially, emotional features are extracted from the images and assigned varying K-values. Subsequently, an encoder-decoder architecture is utilized to derive sentiment features from abstract paintings, augmented by a pre-trained model to enhance classification model generalization and convergence speed. By incorporating a blank attention mechanism into the decoder and integrating it with the encoder's output sequence, the semantics of abstract painting images are learned, facilitating precise and sensible emotional understanding. Experimental results demonstrate that the classification algorithm, utilizing the attention mechanism, achieves a higher accuracy of 80.7% compared to current methods. This innovative approach successfully addresses the intricate challenge of discerning emotions in abstract paintings, underscoring the significance of considering subjective emotional responses in image classification. The integration of advanced techniques such as weighted closest neighbor algorithm and attention mechanisms holds promise for enhancing the comprehension and classification of emotional content in visual art.
基于加权 K 近邻关注机制的图像情感分布预测
现有的图像情绪分类方法往往忽视情绪对观察者的主观影响,而主要关注情绪类别。然而,这种方法忽略了图像中细微的情感反应,无法满足实际需要。本研究提出了一种采用加权近邻算法预测抽象绘画中情感离散分布的新方法。首先,从图像中提取情感特征并赋予不同的 K 值。随后,利用编码器-解码器架构从抽象绘画中提取情感特征,并通过预训练模型增强分类模型的泛化和收敛速度。通过在解码器中加入空白关注机制,并将其与编码器的输出序列相结合,可以学习抽象绘画图像的语义,从而促进精确、合理的情感理解。实验结果表明,与现有方法相比,利用注意力机制的分类算法的准确率高达 80.7%。这一创新方法成功地解决了辨别抽象画中情感这一复杂难题,强调了在图像分类中考虑主观情感反应的重要性。加权近邻算法和注意力机制等先进技术的整合有望提高对视觉艺术中情感内容的理解和分类。
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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