  Customizable image retrieval from large datasets remains a critical
challenge, particularly when preserving spatial relationships within images.
Traditional hashing methods, primarily based on deep learning, often fail to
capture spatial information adequately and lack transparency. In this paper, we
introduce NeuroHash, a novel neuro-symbolic framework leveraging
Hyperdimensional Computing (HDC) to enable highly customizable, spatially-aware
image retrieval. NeuroHash combines pre-trained deep neural network models with
HDC-based symbolic models, allowing for flexible manipulation of hash values to
support conditional image retrieval. Our method includes a self-supervised
context-aware HDC encoder and novel loss terms for optimizing lower-dimensional
bipolar hashing using multilinear hyperplanes. We evaluate NeuroHash on two
benchmark datasets, demonstrating superior performance compared to
state-of-the-art hashing methods, as measured by mAP@5K scores and our newly
introduced metric, mAP@5Kr, which assesses spatial alignment. The results
highlight NeuroHash's ability to achieve competitive performance while offering
significant advantages in flexibility and customization, paving the way for
more advanced and versatile image retrieval systems.
