Human-Oriented Image Retrieval System (HORSE): A Neuro-Symbolic Approach to Optimizing Retrieval of Previewed Images

Abstract

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.