We present a new approach for efficient approximate nearest neighbor (ANN) search in high
dimensional spaces, extending the idea of Product Quantization. We propose a two level …

Approximate nearest neighbor (ANN) search in high dimensions is an integral part of
several computer vision systems and gains importance in deep learning with explicit …

In clustering the training sequence (TS), K-means algorithm tries to find empirically optimal
representative vectors that achieve the empirical minimum to inductively design optimal …

GPU-based methods represent state-of-the-art in approximate nearest neighbor (ANN)
search, as they are scalable (billion-scale), accurate (high recall) as well as efficient (sub …

In designing a vector quantizer using a training sequence (TS), the training algorithm tries to
find an empirically optimal quantizer that minimizes the selected distortion criteria using the …

Vector quantization (VQ) is an efficient data compression technique for low bit rate
applications. However the major disadvantage of VQ is that its encoding complexity …

Channel-optimized vector quantization (COVQ) has proven to be an effective joint source-
channel coding technique that makes the underlying quantizer robust to channel noise …

The vector quantizer (VQ) codebook is usually designed by clustering a training sequence
(TS) drawn from the underlying distribution function. In order to cluster a TS, we may use the …

The problem of designing an optimal vector quantizer when there is access to the noise-free
source has been well studied over the past five decades. However, in many real-world …

An encoding scheme is presented based on random coding theory in conjunction with
companding techniques. This combination provides an easy design, and a low-storage …