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CBIR Feature Vector Dimension Reduction with Eigenvectors of Covariance Matrix using Row, Column and Diagonal Mean Sequences

Author(s): Dr. H.B.Kekre | Sudeep D. Thepade | Akshay Maloo

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: 3;
Issue: 12;
Start page: 39;
Date: 2010;
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Keywords: CBIR | PCA | Eigenvectors | Row Mean | Column Mean | Diagonal Mean

Because of the rising demand from wide range of applications theneed of faster and better image retrieval techniques is growing dayby day. Dimension reduction of CBIR feature vectors has gainedmomentum for swift image retrieval. The paper presents few noveltechniques for image retrieval based on principal componentanalysis (PCA). Here feature vectors are eigenvectors ofcovariance matrix obtained using the row mean, column mean,forward diagonal mean, backward diagonal mean and meancombinations of database images. Instead of taking all pixels ofdatabase images for PCA, proposed CBIR methods use meanvectors, thus dimension of feature vectors used for image retrievalis reduced resulting in faster retrieval. The proposed CBIRtechniques are tested on two different image databases, generalimage database (1000 images spread across 11 categories) andCOIL image database (1080 images spread across 15 objectcategories). For each proposed CBIR technique 55 queries are firedon general image database, 75 queries are fired on COIL imagedatabase and net average precision and recall are computed. Theexperimental results show that proposed CBIR techniques givesthe better performance in terms of higher precision and recallvalues with lesser computational complexity than the conventionalPCA based CBIR using complete image data.
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