International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 43 - Issue 22 |
Published: April 2012 |
Authors: H.B.Kekre, Dhirendra Mishra, Rohan Shah, Shikha Shah, Chirag Thakkar |
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H.B.Kekre, Dhirendra Mishra, Rohan Shah, Shikha Shah, Chirag Thakkar . CBIR using Combined Feature Vectors of Column-Wise and Row-Wise DCT Transformed Plane Sectorization. International Journal of Computer Applications. 43, 22 (April 2012), 35-41. DOI=10.5120/6405-8874
@article{ 10.5120/6405-8874, author = { H.B.Kekre,Dhirendra Mishra,Rohan Shah,Shikha Shah,Chirag Thakkar }, title = { CBIR using Combined Feature Vectors of Column-Wise and Row-Wise DCT Transformed Plane Sectorization }, journal = { International Journal of Computer Applications }, year = { 2012 }, volume = { 43 }, number = { 22 }, pages = { 35-41 }, doi = { 10.5120/6405-8874 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2012 %A H.B.Kekre %A Dhirendra Mishra %A Rohan Shah %A Shikha Shah %A Chirag Thakkar %T CBIR using Combined Feature Vectors of Column-Wise and Row-Wise DCT Transformed Plane Sectorization%T %J International Journal of Computer Applications %V 43 %N 22 %P 35-41 %R 10.5120/6405-8874 %I Foundation of Computer Science (FCS), NY, USA
Content Based Image Retrieval is a way of computer viewing technique used to retrieve digital images from a huge database. In this paper we have first calculated the feature vector column-wise and row-wise separately. After this we have concatenated the feature vectors of column-wise and row-wise. To evaluate the performance of the proposed method we have used Precision-Recall crossover point, LIRS, LSRR and LSRI. Sum of Absolute Distance and Euclidean Distance are the two similarity measures used. The column-row wise DCT transformed image is sectorized on the basis of even-odd column components of transformed image with augmentation of zero and highest row components. The proposed algorithm is applied to a database of thousand images. These thousand images are grouped in ten different classes. Performance is evaluated and compared for 4, 8, 12, 16 DCT sectors.