Cluster Guided Truncated Hashing for Enhanced Approximate Nearest Neighbor Search

Hashing is essential for approximate nearest neighbor search by mapping high-dimensional data to compact binary codes. The balance between similarity preservation and code diversity is a key challenge. Existing projection-based methods often struggle with fitting binary codes to continuous space due to space heterogeneity. To address this, we propose a novel Cluster Guided Truncated Hashing (CGTH) method that uses latent cluster information to guide the binary learning process. By leveraging data clusters as anchor points and applying a truncated coding strategy, our method effectively maintains local similarity and code diversity. Experiments on benchmark datasets demonstrate that CGTH outperforms existing methods, achieving superior search performance.