用脑科学+符号计算，让图像搜索更符合人记忆方式。
Human-Oriented Image Retrieval System (HORSE): A Neuro-Symbolic Approach to Optimizing Retrieval of Previewed Images
- 结合神经网络与符号推理，构建类人记忆的索引机制
- 通过认知科学优化检索流程，提升对预览图的召回效率
- 适合设计纠错、知识管理等需要精准视觉回忆的场景
图像检索因人类视觉感知、记忆与计算过程间的复杂交互而仍具挑战性。现有图像搜索引擎在基于自然语言描述检索时效率低下，常依赖耗时的预处理、打标和机器学习流程。本文提出面向人类的图像检索系统HORSE（Human-Oriented Retrieval Search Engine for Images），采用神经符号索引方法，聚焦人类认知导向的索引策略。该框架融合认知科学洞见与先进计算技术，增强检索过程，使其更贴合人类对视觉信息的感知、存储与回忆方式。神经符号架构结合了神经网络的泛化能力与符号推理的可解释性，弥补二者局限。系统优化了图像检索效率，提供更直观、高效的用户解决方案。文中讨论了HORSE的设计与实现，强调其在设计错误检测、知识管理等领域的应用潜力，并提出未来研究方向以进一步提升系统指标与能力。
原文摘要 · Abstract (English)
Image retrieval remains a challenging task due to the complex interaction between human visual perception, memory, and computational processes. Current image search engines often struggle to efficiently retrieve images based on natural language descriptions, as they rely on time-consuming preprocessing, tagging, and machine learning pipelines. This paper introduces the Human-Oriented Retrieval Search Engine for Images (HORSE), a novel approach that leverages neuro-symbolic indexing to improve image retrieval by focusing on human-oriented indexing. By integrating cognitive science insights with advanced computational techniques, HORSE enhances the retrieval process, making it more aligned with how humans perceive, store, and recall visual information. The neuro-symbolic framework combines the strengths of neural networks and symbolic reasoning, mitigating their individual limitations. The proposed system optimizes image retrieval, offering a more intuitive and efficient solution for users. We discuss the design and implementation of HORSE, highlight its potential applications in fields such as design error detection and knowledge management, and suggest future directions for research to further refine the system's metrics and capabilities.
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