用视觉特征预测食物偏好，准确率达96%
IMVB7t: A Multi-Modal Model for Food Preferences based on Artificially Produced Traits
- 融合五种模型提取环境图像特征，构建集成检测系统
- 基于用户调研数据，决策树推荐菜品准确率96%
- 为饮食行为研究提供可扩展的多模态分析框架
人类行为与互动深受周围视觉刺激的影响，尤其体现在食物消费与选择上。本研究采用多种模型从环境图像中提取不同属性，识别出五个关键特征，并基于五个独立模型构建集成模型IMVB7，其检测准确率达到0.85。同时，通过问卷调查分析视觉刺激对食物偏好的影响模式，结合识别出的属性，利用决策树生成菜品推荐，最终构建的IMVB7t模型在推荐任务上达到0.96的准确率。该研究为跨学科领域探索提供了基础性范式。
原文摘要 · Abstract (English)
Human behavior and interactions are profoundly influenced by visual stimuli present in their surroundings. This influence extends to various aspects of life, notably food consumption and selection. In our study, we employed various models to extract different attributes from the environmental images. Specifically, we identify five key attributes and employ an ensemble model IMVB7 based on five distinct models for some of their detection resulted 0.85 mark. In addition, we conducted surveys to discern patterns in food preferences in response to visual stimuli. Leveraging the insights gleaned from these surveys, we formulate recommendations using decision tree for dishes based on the amalgamation of identified attributes resulted IMVB7t 0.96 mark. This study serves as a foundational step, paving the way for further exploration of this interdisciplinary domain.
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