DocumentCode :
327697
Title :
Which ranking metric is optimal? With applications in image retrieval and stereo matching
Author :
Sebe, N. ; Lew, M. ; Huijsmans, D.P.
Author_Institution :
Dept. of Comput. Sci., Leiden Univ., Netherlands
Volume :
1
fYear :
1998
fDate :
16-20 Aug 1998
Firstpage :
265
Abstract :
Euclidean metric is frequently used in computer vision, mostly ad-hoc without any justification. However we have found that other metrics like a double exponential or Cauchy metric provide better results, in accordance with the maximum likelihood approach. In this paper we experiment with different modeling functions for similarity noise and compute the accuracy of different methods using these modeling functions in two kinds of applications: content-based image retrieval from a large database and stereo matching. We provide a way to determine the modeling distribution which fits best the similarity noise distribution according to the ground truth. In the optimum case, when one has chosen the best modeling distribution, its corresponding metric will give the best ranking results for the ground truth provided
Keywords :
computer vision; image matching; maximum likelihood estimation; query processing; stereo image processing; visual databases; computer vision; content-based query; image retrieval; intensity space; maximum likelihood estimation; modeling functions; parametrised metric; similarity noise distribution; stereo matching; visual database; Application software; Computer science; Digital images; Feature extraction; Histograms; Image databases; Image retrieval; Information retrieval; Integrated circuit noise; Spatial databases;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
Conference_Location :
Brisbane, Qld.
ISSN :
1051-4651
Print_ISBN :
0-8186-8512-3
Type :
conf
DOI :
10.1109/ICPR.1998.711132
Filename :
711132
Link To Document :
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