Wei Lei
School of Art and Design, Pingdingshan University, Pingdingshan, Henan, 467000, China.
Heliyon. 2024 Jun 7;10(12):e32674. doi: 10.1016/j.heliyon.2024.e32674. eCollection 2024 Jun 30.
Color plays a pivotal role in product design, as it can evoke emotional responses from users. Understanding these emotional needs is crucial for effective brand image design. This paper introduces a novel approach, the Brand Image Design using Deep Multi-Scale Fusion Neural Network optimized with Cheetah Optimization Algorithm (BID-DMSFNN-COA), for classifying product color brand images as "Stylish" and "Natural". By leveraging deep learning techniques and optimization algorithms, the proposed method aims to enhance brand image accuracy and address existing challenges in product color trend forecasting research. Initially, data are collected from the Mnist Data Set. The data are then supplied into the pre-processing section. In the pre-processing segment, it removes the noise and enhances the input image utilizing master slave adaptive notch filter. The Deep Multi-Scale Fusion Neural Network optimized with cheetah optimization algorithm effectively classifies the product colour brand image as "Stylish" and "Natural". Implemented on the MATLAB platform, the BID-DMSFNN-COA technique achieves remarkable accuracy rates of 99 % for both "Natural" and "Stylish" classifications. In comparison, existing methods such as BID-GNN, BID-ANN, and BID-CNN yield lower accuracy rates ranging from 65 % to 85 % for "Stylish" and 65 %-70 % for "Natural" product color brand image design. The simulation outcomes reveal the superior performance of the BID-DMSFNN-COA technique across various metrics including accuracy, F-score, precision, recall, sensitivity, specificity, and ROC analysis. Notably, the proposed method consistently outperforms existing approaches, providing higher values across all evaluation criteria. These findings underscore the effectiveness of the BID-DMSFNN-COA technique in enhancing brand image design through accurate product color classification.
色彩在产品设计中起着关键作用，因为它能唤起用户的情感反应。了解这些情感需求对于有效的品牌形象设计至关重要。本文介绍了一种新颖的方法，即使用经猎豹优化算法优化的深度多尺度融合神经网络进行品牌形象设计（BID-DMSFNN-COA），用于将产品色彩品牌图像分类为“时尚”和“自然”。通过利用深度学习技术和优化算法，该方法旨在提高品牌形象的准确性，并解决产品色彩趋势预测研究中现有的挑战。首先，从Mnist数据集中收集数据。然后将数据提供给预处理部分。在预处理阶段，使用主从自适应陷波滤波器去除噪声并增强输入图像。经猎豹优化算法优化的深度多尺度融合神经网络有效地将产品色彩品牌图像分类为“时尚”和“自然”。在MATLAB平台上实现后，BID-DMSFNN-COA技术在“自然”和“时尚”分类中均达到了99%的显著准确率。相比之下，现有的方法如BID-GNN、BID-ANN和BID-CNN在“时尚”产品色彩品牌图像设计中的准确率较低，在65%至85%之间，在“自然”产品色彩品牌图像设计中的准确率在65%至70%之间。仿真结果表明，BID-DMSFNN-COA技术在包括准确率、F分数、精确率、召回率、灵敏度、特异性和ROC分析等各种指标上具有卓越的性能。值得注意的是，所提出的方法始终优于现有方法，在所有评估标准上都提供了更高的值。这些发现强调了BID-DMSFNN-COA技术通过准确的产品色彩分类在增强品牌形象设计方面的有效性。
