Fast search in Hamming space with multi-index hashing

Norouzi, Mohammad; Punjani, Ali; Fleet, David J.

doi:10.1109/cvpr.2012.6248043

Cited by 235 publications

(269 citation statements)

References 14 publications

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“…Experimental results can validate the claims and verify the algorithm's optimality. In fact, many questions can only be answered after the application of the algorithm on data [14,15,16], such as:…”

Section: Future Workmentioning

confidence: 99%

An Efficient DNA Molecule Clustering using GCC Algorithm

Kholood

Alnowaiser

2015

GSTF J Comput

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Section: Future Workmentioning

confidence: 99%

An Efficient DNA Molecule Clustering using GCC Algorithm

Kholood

Alnowaiser

2015

GSTF J Comput

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“…Our proposed method of using multiple binary codes does not conflict with the existing MIH [3], [4]; we can, therefore, integrate multiple binary codes and MIH to enhance the search speed and performance. In more detail, each binary code is split into m sub-strings, and the b×m hash tables for each sub-string are built in advance.…”

Section: Multiple Binary Codes With Mihmentioning

confidence: 99%

“…Since the code length should generally be long to achieve an accurate similarity search as in the original data space, the hash table approach tends to be impractical. Norouzi et al have proposed multi-index hashing (MIH) [3], [4] that uses multiple hash tables for one binary code to accelerate the computational speed of an exact similarity search in the Hamming space. In MIH, the binary code is split into m sub-strings.…”

Section: Similarity Search Using a Binary Codementioning

confidence: 99%

Multiple Binary Codes for Fast Approximate Similarity Search

Shirakawa

2015

IEICE Trans. Inf. & Syst.

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SUMMARYOne of the fast approximate similarity search techniques is a binary hashing method that transforms a real-valued vector into a binary code. The similarity between two binary codes is measured by their Hamming distance. In this method, a hash table is often used when undertaking a constant-time similarity search. The number of accesses to the hash table, however, increases when the number of bits lengthens. In this paper, we consider a method that does not access data with a long Hamming radius by using multiple binary codes. Further, we attempt to integrate the proposed approach and the existing multi-index hashing (MIH) method to accelerate the performance of the similarity search in the Hamming space. Then, we propose a learning method of the binary hash functions for multiple binary codes. We conduct an experiment on similarity search utilizing a dataset of up to 50 million items and show that our proposed method achieves a faster similarity search than that possible with the conventional linear scan and hash table search. key words: similarity search, binary hash, approximate nearest neighbor search, machine learning, image retrieval

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“…However, semantic hashing is largely practical only for searching for nearest neighbours that differ by only a couple of bits. Another prominent approach in the use of hashing techniques is multi-index hashing, presented in [15] and which showed results on datasets with binary strings of lengths up to 128 bits. In general, however, it is also the length of the binary strings and the amount of memory required to store all the buckets for a hashing approach that often serves as the limitation of a hashing approach.…”

Section: Related Workmentioning

confidence: 99%

Fast Matching of Binary Descriptors for Large-Scale Applications in Robot Vision

Persson

Loutfi

2016

International Journal of Advanced Robotic Systems

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The introduction of computationally efficient binary feature descriptors has raised new opportunities for realworld robot vision applications. However, brute force feature matching of binary descriptors is only practical for smaller datasets. In the literature, there has therefore been an increasing interest in representing and matching binary descriptors more efficiently. In this article, we follow this trend and present a method for efficiently and dynamically quantizing binary descriptors through a summarized frequency count into compact representations (called fsum) for improved feature matching of binary pointfeatures. With the motivation that real-world robot applications must adapt to a changing environment, we further present an overview of the field of algorithms, which concerns the efficient matching of binary descriptors and which are able to incorporate changes over time, such as clustered search trees and bag-of-features improved by vocabulary adaptation. The focus for this article is on evaluation, particularly large scale evaluation, compared to alternatives that exist within the field. Throughout this evaluation it is shown that the fsum approach is both efficient in terms of computational cost and memory requirements, while retaining adequate retrieval accuracy. It is further shown that the presented algorithm is equally suited to binary descriptors of arbitrary type and that the algorithm is therefore a valid option for several types of vision applications.

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Fast search in Hamming space with multi-index hashing

Cited by 235 publications

References 14 publications

An Efficient DNA Molecule Clustering using GCC Algorithm

An Efficient DNA Molecule Clustering using GCC Algorithm

Multiple Binary Codes for Fast Approximate Similarity Search

Fast Matching of Binary Descriptors for Large-Scale Applications in Robot Vision

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