vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff73e51b000000d51a000001000500 This paper proposes a novel technique based on feature fusion using multiple linear regression analysis, and the least-square estimation method is employed to estimate the parameters. The given input query image is segmented into various regions according to the structure of the image. The color and texture features are extracted on each region of the query image, and the features are fused together using the multiple linear regression model. The estimated parameters of the model, which is modeled based on the features, are formed as a vector called a feature vector. The Canberra distance measure is adopted to compare the feature vectors of the query and target images. The F-measure is applied to evaluate the performance of the proposed technique. The obtained results expose that the proposed technique is comparable to the other existing techniques.
K. Seetharaman1, R. Shekhar2
Annamalai University, India1, Manonmaniam Sundaranar University, India2
Regression, Fusion, Feature, F-measure, Least-Square Estimate
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 6 , Issue: 1 , Pages: 1066-1071 )
Date of Publication :
August 2015
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