2011/10/17 by Hervé Jégou, Jégou, Hervé, Teddy Furon +3 · 1 citation
Computer Science · Mathematics · #Advanced Data Compression Techniques #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #Databases (cs.DB) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Information Theory (cs.IT) #Medical Image Segmentation Techniques #cs.CV #cs.DB #cs.IR #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.1110.3767
submitted to ICASSP'2012; RR-7771 (2011)
openalex publication_date 2011/10/17 · arxiv created 2011/10/25 · arxiv updated 2011/10/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper proposes a binarization scheme for vectors of high dimension based on the recent concept of anti-sparse coding, and shows its excellent performance for approximate nearest neighbor search. Unlike other binarization schemes, this framework allows, up to a scaling factor, the explicit reconstruction from the binary representation of the original vector. The paper also shows that random projections which are used in Locality Sensitive Hashing algorithms, are significantly outperformed by regular frames for both synthetic and real data if the number of bits exceeds the vector dimensionality, i.e., when high precision is required.