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在品牌形象设计中应用DMSFNN-COA技术

¹School of Art and Design, Pingdingshan University, Pingdingshan, Henan, 467000, China.

|July 18, 2024

概括

一个新的深度学习模型,品牌形象设计使用深度多尺度融合神经网络优化与猎优化算法 (BID-DMSFNN-COA),准确地将产品颜色分类为"时尚"或"自然". 这种方法显著改进了现有的品牌形象设计和色彩趋势预测技术.

科学领域:

计算机科学计算机科学
人工智能的人工智能
机器学习机器学习

背景情况:

颜色显著影响用户的情绪,对于有效的品牌形象设计至关重要.
准确的产品颜色分类对于了解用户偏好和预测趋势至关重要.
现有的产品颜色品牌形象分类方法在实现高精度方面面临挑战.

研究的目的:

引入一种新的深度学习方法,BID-DMSFNN-COA,用于对产品颜色品牌图像进行分类.
通过有效地将颜色分类为"时尚"或"自然"来提高品牌形象设计的准确性.
为了解决当前产品颜色趋势预测研究的局限性.

主要方法:

使用Mnist数据集进行图像数据收集和预处理,包括使用主-奴隶自适应性口过器降噪.
开发并实施了使用深度多尺度融合神经网络优化与猎优化算法 (BID-DMSFNN-COA) 的品牌形象设计.
采用深度学习和优化算法来进行强大的图像分类.

主要成果:

BID-DMSFNN-COA技术在"自然"和"时尚"分类方面实现了99%的精度.
超越了现有的方法 (BID-GNN,BID-ANN,BID-CNN),其准确率较低 (65%-85%).
在多个评估指标上表现出卓越的表现,包括准确性,F-score,精度,回忆,灵敏度,特异性和ROC分析.

结论:

BID-DMSFNN-COA技术为产品颜色品牌形象分类提供了高度有效和准确的解决方案.
拟议的方法通过提高颜色分类的精度,显著提高了品牌形象设计.
这一进步为产品颜色趋势预测和品牌战略开发提供了更可靠的工具.

关键词:

自动图像分类自动图像分类品牌形象设计品牌形象设计猎优化算法猎优化算法深度多尺度融合神经网络深度神经网络主-奴隶自适应口过器 Mnist 数据集是一个数据集.时尚和自然的产品颜色品牌形象品牌形象

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