Distance learning for similarity estimation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Jie YuQi Tian

Abstract

In this paper, we present a general guideline to find a better distance measure for similarity estimation based on statistical analysis of distribution models and distance functions. A new set of distance measures are derived from the harmonic distance, the geometric distance, and their generalized variants according to the Maximum Likelihood theory. These measures can provide a more accurate feature model than the classical Euclidean and Manhattan distances. We also find that the feature elements are often from heterogeneous sources that may have different influence on similarity estimation. Therefore, the assumption of single isotropic distribution model is often inappropriate. To alleviate this problem, we use a boosted distance measure framework that finds multiple distance measures which fit the distribution of selected feature elements best for accurate similarity estimation. The new distance measures for similarity estimation are tested on two applications: stereo matching and motion tracking in video sequences. The performance of boosted distance measure is further evaluated on several benchmark data sets from the UCI repository and two image retrieval applications. In all the experiments, robust results are obtained ba...Continue Reading

References

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Citations

Mar 20, 2010·IEEE Transactions on Pattern Analysis and Machine Intelligence·Xiang BaiZhuowen Tu
Jul 20, 2011·IEEE Transactions on Pattern Analysis and Machine Intelligence·Salvador GarcíaFrancisco Herrera
Apr 7, 2018·FEBS Letters·Charlotte MignonBettina Werle
May 14, 2020·Journal of Digital Imaging·Muhammad KashifFurqan Shaukat
May 6, 2019·Computers in Biology and Medicine·Zhenwei Zhang, Ervin Sejdić
May 7, 2021·Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy·Yue SunLei Nie

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