Work

Billion-Scale Similarity Search Using a Hybrid Indexing Approach with Advanced Filtering

Year: 2024

Type: article

Abstract: This paper presents a novel approach for similarity search with complex filtering capabilities on billion-scale datasets, optimized for CPU inference. Our method extends the classical IVF-Flat index structure to integrate multi-dimensional filters. The proposed algorithm combines dense embeddings with discrete filtering attributes, enabling fast retrieval in high-dimensional spaces. Designed specifically for CPU-based systems, our disk-based approach offers a cost-effective solution for large-scale similarity search. We demonstrate the effectiveness of our method through a case study, showcasing its potential for various practical uses.

Source: Cybernetics and Information Technologies

DOI: 10.2478/cait-2024-0035

Authors: Simeon Emanuilov, Aleksandar Dimov

Institution: Sofia University "St. Kliment Ohridski"

Language: English

Topic: Advanced Image and Video Retrieval Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: European Commission

Awards: G2566934999, G4188552653