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Reduction in Feature Vector Size of Colour Averaging based Image Retrieval Techniques using Walsh Wavelet Pyramid Levels

Author(s): Dr. H. B. Kekre | Dr. Sudeep D. Thepade | Dr. Tanuja K. Sarode | Varun K. Banura

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: 32;
Issue: 3;
Start page: 28;
Date: 2011;
Original page

Keywords: CBIR | Colour averaging | Row and Column Mean (RCM) | Forward Diagonal Mean (FDM) | Wavelet Pyramids | Walsh Transform

The paper presents the reduction of feature vector size of the image by using Wavelet Pyramids based image retrieval techniques for Walsh Transform. The colour averaging methods like row and column mean 'RCM', forward diagonal mean 'FDM' and row column and forward diagonal mean 'RCFDM' are applied on image wavelets generated at four levels of decomposition. The proposed content based image retrieval 'CBIR' techniques are tested on a generic image database having 1000 images spread across 11 categories. For each proposed CBIR technique 55 queries 'randomly selected 5 per category' are fired on the image database. To compare the performance of image retrieval techniques average precision and recall values are computed for all queries. When these results are compared with the colour averaging based image retrieval techniques applied on the original image itself, it has been observed that the precision recall crossover value for wavelet pyramid level 1 is almost same 'up to 3 decimal places' for FDM and RCFDM. However the size of the feature vector in the proposed CBIR methods is significantly less than the original image. Thus the proposed CBIR methods prove to be better in terms of reduced computational complexity. In the discussed image retrieval methods, Walsh wavelet pyramid level 1 for RCFDM gives the highest performance as indicated by the precision recall crossover point.
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