Abstract
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.
Abstract (translated)
面对快速增长的图像数据，高效地检索相似的图像是一个具有挑战性的任务。过去的研究所侧重于优化哈希函数，以将图像压缩成相似性的简洁指标。初始尝试使用浅层模型，从卷积神经网络（CNNs）进化到关注机制为基础的架构，最终达到更先进的模型。然而，对于基于梯度的模型的空间信息嵌入限制，我们提出了创新性的图像哈希方法：NeuroHash，利用高维计算（HDC）。HDC 符号化地编码空间信息为高维向量，重新塑造图像表示。我们的方法将预训练的大视觉模型与 HDC 操作相结合，实现了空间编码特征表示。使用局部感知哈希（LSH）进行哈希确保快速且高效的图像检索。值得注意的是，我们的框架允许动态哈希操作进行条件图像检索。我们的工作引入了一个 transformative 图像哈希框架，实现空间感知条件检索。通过将基于深度神经网络（DNN）的神经模型与基于高维计算（HDC）的符号模型无缝结合，我们的方法摒弃了传统的训练方式，实现了灵活的带有条件图像检索。性能评估表明，图像哈希方法论正处于一种范式性的转变，并证明了更准确的检索精度。
URL
https://arxiv.org/abs/2404.11025
