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JACIII Vol.22 No.5 pp. 689-698
doi: 10.20965/jaciii.2018.p0689
(2018)

Paper:

A Filter of Minhash for Image Similarity Measures

Jun Long*, Qunfeng Liu*, Xinpan Yuan**,†, Chengyuan Zhang*, and Junfeng Liu*

*School of Information Science and Engineering, Central South University
Changsha City, Hunan Province 410083, China

**School of Computer, Hunan University of Technology
Zhuzhou City, Hunan Province 412007, China

Corresponding author

Received:
March 23, 2018
Accepted:
June 13, 2018
Published:
September 20, 2018
Keywords:
image similarity measures, BoVW, SIFT, Minwise Hashing, dynamic threshold filter
Abstract

Image similarity measures play an important role in nearest neighbor search and duplicate detection for large-scale image datasets. Recently, Minwise Hashing (or Minhash) and its related hashing algorithms have achieved great performances in large-scale image retrieval systems. However, there are a large number of comparisons for image pairs in these applications, which may spend a lot of computation time and affect the performance. In order to quickly obtain the pairwise images that theirs similarities are higher than the specific threshold T (e.g., 0.5), we propose a dynamic threshold filter of Minwise Hashing for image similarity measures. It greatly reduces the calculation time by terminating the unnecessary comparisons in advance. We also find that the filter can be extended to other hashing algorithms, on when the estimator satisfies the binomial distribution, such as b-Bit Minwise Hashing, One Permutation Hashing, etc. In this pager, we use the Bag-of-Visual-Words (BoVW) model based on the Scale Invariant Feature Transform (SIFT) to represent the image features. We have proved that the filter is correct and effective through the experiment on real image datasets.

Cite this article as:
J. Long, Q. Liu, X. Yuan, C. Zhang, and J. Liu, “A Filter of Minhash for Image Similarity Measures,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.5, pp. 689-698, 2018.
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Last updated on Oct. 23, 2018