DocumentCode :
949462
Title :
Distance Learning for Similarity Estimation
Author :
Yu, Jie ; Amores, Jaume ; Sebe, Nicu ; Radeva, Petia ; Tian, Qi
Author_Institution :
Kodak Res. Labs, Rochester
Volume :
30
Issue :
3
fYear :
2008
fDate :
3/1/2008 12:00:00 AM
Firstpage :
451
Lastpage :
462
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 based on the proposed methods.
Keywords :
maximum likelihood estimation; statistical analysis; statistical distributions; distance functions; distance learning; distance measures; generalized variants; geometric distance; harmonic distance; maximum likelihood theory; motion tracking; similarity estimation; single isotropic distribution model; statistical analysis; stereo matching; video sequence; Algorithms; Artificial intelligence; Image classification; Information retrieval; Pattern recognition; Algorithms; Artificial Intelligence; Data Interpretation, Statistical; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Pattern Recognition, Automated; Photogrammetry; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/TPAMI.2007.70714
Filename :
4359335
Link To Document :
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