Image Similarity Measures

Improving image similarity measures for image browsing and retrieval through latent space learning between images and long texts

2010 IEEE International Conference on Image Processing ◽

10.1109/icip.2010.5652827 ◽

2010 ◽

Author(s):

Yoshitaka Ushiku ◽

Tatsuya Harada ◽

Yasuo Kuniyoshi

Keyword(s):

Similarity Measures ◽

Image Similarity ◽

Latent Space ◽

Image Browsing

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A Filter of Minhash for Image Similarity Measures

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2018.p0689 ◽

2018 ◽

Vol 22 (5) ◽

pp. 689-698

Author(s):

Jun Long ◽

Qunfeng Liu ◽

Xinpan Yuan ◽

Chengyuan Zhang ◽

Junfeng Liu ◽

...

Keyword(s):

Large Scale ◽

Nearest Neighbor ◽

Similarity Measures ◽

Computation Time ◽

Image Features ◽

Nearest Neighbor Search ◽

Image Similarity ◽

Scale Invariant ◽

Minwise Hashing ◽

Image Datasets

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 thresholdT(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.

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