RFHNet：用于大规模细粒度食品图像检索的关系与频率感知哈希网络
RFHNet: Relational and Frequency-Aware Hashing Network for Large-Scale Fine-Grained Food Image Retrieval
AI总结：
针对细粒度食品图像检索难题，提出RFHNet，通过级联分层哈希网络，利用细粒度关系建模、多频调制融合和分层语义协同三个组件，有效捕获全局与局部细节，实验证明其性能优于现有方法，提升了大规模视觉食品检索效果。
摘要：
细粒度食品图像检索是计算美食学中的关键任务，在食品可追溯性、饮食监测和智能餐饮系统中有应用。基于哈希的检索虽因存储效率和快速汉明距离计算对大规模搜索有吸引力，但现有方法在细粒度食品场景中表现不佳。为此提出RFHNet，一个级联分层哈希网络，通过多级表示捕获全局结构和细粒度局部细节。它包含细粒度关系建模、多频调制融合和分层语义协同三个组件。在六个食品特定基准上的实验表明，RFHNet持续优于现有哈希方法，在12位时mAP增益为4.44%至17.20%。这些结果验证了RFHNet对大规模视觉食品检索和智能餐饮应用的有效性。源代码将在发表时发布。
英文摘要：
Fine-grained food image retrieval is a key task in computational gastronomy, with applications in food traceability, dietary monitoring, and smart catering systems. Although hashing-based retrieval is attractive for large-scale search due to its storage efficiency and fast Hamming-distance computation, existing methods often perform poorly in fine-grained food scenarios, where subtle local semantics and frequency-sensitive visual cues are essential. To address this challenge, we propose RFHNet, a cascaded hierarchical hashing network that captures both global structure and fine-grained local details through multi-level representations. RFHNet includes three components: (1) Fine-grained Relation Modeling (FRM) to capture subtle visual differences among similar food components; (2) Multi-Frequency Modulated Fusion (MFMF) to extract informative multi-frequency features; and (3) Hierarchical Semantic Synergy (HSS) to adaptively integrate multi-level representations and generate discriminative hash codes. Experiments on six food-specific benchmarks show that RFHNet consistently outperforms state-of-the-art hashing methods, with mAP gains of 4.44\% to 17.20\% at 12 bits. These results validate the effectiveness of RFHNet for large-scale visual food retrieval and smart catering applications. The source code will be released upon publication.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
