Motivated by large-scale multimedia applications we propose to learn map**s from high-
dimensional data to binary codes that preserve semantic similarity. Binary codes are well …

Product quantization (PQ) is an effective vector quantization method. A product quantizer
can generate an exponentially large codebook at very low memory/time cost. The essence …

This article presents a comprehensive review and analysis on image search in the past 20
years, emphasizing the challenges and opportunities brought by the astonishing increase of …

This paper addresses the problem of similar image retrieval, especially in the setting of large-
scale datasets with millions to billions of images. The core novel contribution is an approach …

Compact hash code learning has been widely applied to fast similarity search owing to its
significantly reduced storage and highly efficient query speed. However, it is still a …

Binary coding or hashing techniques are recognized to accomplish efficient near neighbor
search, and have thus attracted broad interests in the recent vision and learning studies …

Recent advances in visual recognition indicate that to achieve good retrieval and
classification accuracy on largescale datasets like ImageNet, extremely high-dimensional …

Learning to hash has attracted broad research interests in recent computer vision and
machine learning studies, due to its ability to accomplish efficient approximate nearest …

In large-scale query-by-example retrieval, embedding image signatures in a binary space
offers two benefits: data compression and search efficiency. While most embedding …

Recently it was shown that the problem of Maximum Inner Product Search (MIPS) is efficient
and it admits provably sub-linear hashing algorithms. Asymmetric transformations before …