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Augmentation of Colour Averaging Based Image Retrieval Techniques using Even part of Images and Amalgamation of feature vectors

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

Journal: International Journal of Engineering Science and Technology
ISSN 0975-5462

Volume: 2;
Issue: 10;
Start page: 5238;
Date: 2010;
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Keywords: CBIR | Colour averaging | Row & Column Mean (RCM) | Forward Diagonal Mean (FDM) | Row Column & Forward Diagonal Mean (RCFDM) | Even Image

The theme of the work presented here is augmentation of the colour averaging based image retrieval techniques using even part of images given in [1]. The reflection of the original image is taken across horizontal and vertical directions to get a flip image. The even part of the image is obtained by adding original and flip images. It is clear from [1] that the combination of original image with even part gives better image retrieval than the original alone. On the other hand the combination of original image with odd part gives the worst results. Thus the colour averaging techniques like row & column mean (RCM), forward diagonal mean (FDM) and row, column & forward diagonalmean (RCFDM) are applied on the original image and the even part of image. The colour averages (feature vectors) of original image are considered in combination with the even colour averages to get proposed original+even CBIR techniques which are compared with CBIR methods given in [1]. 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 (5 per category) are fired on the image database. To compare the performance of image retrieval techniques average precision and recall are computed for all the queries. The results have shown improved performance (higher precision and recall values) with the proposed methods compared to the simple original image feature vectors. In the discussed image retrieval methods original with even proves to be better than the original. The combination of row, column & forward diagonal means (RCFDM) gives the highest performance in the discussed three methods of feature vector selection for respective CBIR methods (original, original with even).
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