Study on an Image Candidate Generation Method Based on PCA-Guided Projection

Abstract

Image retrieval often begins with candidate generation, where a small subset of database images is selected for subsequent reranking or inspection. Standard multi-table p-stable locality-sensitive hashing (LSH) is a classical solution for this stage, but its isotropically sampled projection vectors do not explicitly exploit the principal structure of deep feature distributions. To address this limitation, this paper proposes a PCA-guided candidate generation method using the multi-table bucketization and query architecture of p-stable LSH. Unlike conventional PCA-based feature extraction, PCA is not used here to transform the image features themselves, but to bias the sampling distribution of projection vectors during indexing. Experiments on CALTECH101, CIFAR-10, and Tiny-ImageNet, performed using VGG19, ConvNeXt-Tiny, and ViT-B/16 features, show that the proposed method generally improves on the standard p-stable baseline across different settings. Additional comparisons with HNSW and IVF further position the method relative to modern ANN baselines. The results indicate that PCA-guided projection is an effective lightweight enhancement for systems based on the multi-table p-stable LSH architecture, improving candidate coverage and robustness while remaining compatible with the original online query process.