Ten lectures on wavelets
Rotation and scale invariant texture features using discrete wavelet packet transform
Pattern Recognition Letters
Multiresolution reconstruction in fan-beam tomography
IEEE Transactions on Image Processing
A Bayesian network classifier and hierarchical Gabor features for handwritten numeral recognition
Pattern Recognition Letters
A modified Gabor function for content based image retrieval
Pattern Recognition Letters
Fast Zernike wavelet moments for Farsi character recognition
Image and Vision Computing
Network traffic analysis using singular value decomposition and multiscale transforms
Information Sciences: an International Journal
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
WSEAS Transactions on Information Science and Applications
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The present work aims at proposing a new wavelet representation formula for rotation invariant feature extraction. The algorithm is a multilevel representation formula involving no wavelet decomposition in standard sense. Using the radial symmetry property, that comes inherently in the new representation formula, we generate the feature vectors that are shown to be rotation invariant. We show that, using a hybrid data mining technique, the algorithm can be used for rotation invariant content based image retrieval (CBIR). The proposed rotation invariant retrieval algorithm, suitable for both texture and nontexture images, avoids missing any relevant images but may retrieve some other images which are not very relevant. We show that the higher precision can however be achieved by pruning out irrelevant images.