alexa Abstract | Image Matching Scheme by using Bhattacharyya Coefficient Algorithm
ISSN ONLINE(2320-9801) PRINT (2320-9798)

International Journal of Innovative Research in Computer and Communication Engineering
Open Access

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Research Article Open Access


A recurring problem that arises throughout the sciences is that of deciding whether two statistical distributionsdiffer or these are consistent - currently the chi-squared statistic is the most commonly used technique for addressing thisproblem. This paper explains the drawbacks of the chi-squared statistic for comparing measurements over largedistances in pattern space and suggests that the Bhattacharyya measure can avoid such difficulties. The originalinterpretation of the Bhattacharyya metric as a geometric similarity measure is reviewed and it is pointed out thatthis derivation is independent of the use of the Bhattacharyya measure as an upper bound on misclassification in a Two-class problem. The affinity between the Bhattacharyya measures is described and thatthe measure is applicable to any distribution of data. I explain that the Bhattacharyya measure is consistent withan assumption of a Poisson generation mechanism for individual measurements in a distribution which is applicableto a frequency (histogram) or probabilistic data set and suggest application of the Bhattacharyya measure to thefield of system identification.

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Author(s): Nwe Nwe Soe


Image understanding, Image matching, Attribute Modelling, Image Model Matching, Bhattacharyya Coefficient, Asynchronous Machines,Artificial Intelligence in Electronics.

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