Spatial-Aware Image Retrieval: A Hyperdimensional Computing Approach for
  Efficient Similarity Hashing
Sanggeon Yun Ryozo Masukawa SungHeon Jeong Mohsen Imani
In the face of burgeoning image data, efficiently retrieving similar images
poses a formidable challenge. Past research has focused on refining hash
functions to distill images into compact indicators of resemblance. Initial
attempts used shallow models, evolving to attention mechanism-based
architectures from Convolutional Neural Networks (CNNs) to advanced models.
Recognizing limitations in gradient-based models for spatial information
embedding, we propose an innovative image hashing method, NeuroHash leveraging
Hyperdimensional Computing (HDC). HDC symbolically encodes spatial information
into high-dimensional vectors, reshaping image representation. Our approach
combines pre-trained large vision models with HDC operations, enabling
spatially encoded feature representations. Hashing with locality-sensitive
hashing (LSH) ensures swift and efficient image retrieval. Notably, our
framework allows dynamic hash manipulation for conditional image retrieval. Our
work introduces a transformative image hashing framework enabling spatial-aware
conditional retrieval. By seamlessly combining DNN-based neural and HDC-based
symbolic models, our methodology breaks from traditional training, offering
flexible and conditional image retrieval. Performance evaluations signify a
paradigm shift in image-hashing methodologies, demonstrating enhanced retrieval
accuracy.