An introduction to wavelets
Query by Visual Example - Content based Image Retrieval
EDBT '92 Proceedings of the 3rd International Conference on Extending Database Technology: Advances in Database Technology
Region-based image retrieval using integrated color, shape, and location index
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Color Traits Transfer to Grayscale Images
ICETET '08 Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology
Image retrieval using augmented block truncation coding techniques
Proceedings of the International Conference on Advances in Computing, Communication and Control
Image retrieval by Kekre's transform applied on each row of Walsh transformed VQ codebook
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
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The theme of the work presented here is image retrieval using texture patterns generated from Haar transform matrix and image bitmaps. Different texture patterns namely '4-pattern', '16-pattern', '64-pattern', '256-pattern' and '1024-pattern' are generated using Haar transform matrix and then compared with the bitmap of an image to generate the feature vector as the matching number of ones and minus ones per texture pattern. 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, crossover point of average precision and recall values of all the queries are computed per image retrieval technique. Ameliorated performance (higher precision and recall values) has been observed with the proposed methods compared to the colour averaging based image retrieval techniques. Further the performance of proposed image retrieval methods is enhanced using the combination of original image and even image part. In the discussed image retrieval methods, the combination of original and even image part for 256-pattern texture gives the highest crossover point of precision and recall reflecting better performance.