Photobook: content-based manipulation of image databases
International Journal of Computer Vision
Querying by color regions using VisualSEEk content-based visual query system
Intelligent multimedia information retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation-invariant texture feature for image retrieval
Computer Vision and Image Understanding
NeTra: a toolbox for navigating large image databases
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
Perceptive Visual Texture Classification and Retrieval
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Gaussian MRF Rotation-Invariant Features for Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Pattern Recognition Letters
Rotation-invariant and scale-invariant Gabor features for texture image retrieval
Image and Vision Computing
New Texture Features Based on Wavelet Transform Coinciding with Human Visual Perception
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 01
Texture image retrieval based on non-tensor product wavelet filter banks
Signal Processing
A new integer image coding technique based on orthogonal polynomials
Image and Vision Computing
Texture image retrieval using new rotated complex wavelet filters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rotation-invariant texture classification using a complete space-frequency model
IEEE Transactions on Image Processing
Rotation-invariant texture retrieval with gaussianized steerable pyramids
IEEE Transactions on Image Processing
Journal of Computer and System Sciences
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In this paper, a simple and an efficient Content Based Image Retrieval which is based on orthogonal polynomials model is presented. This model is built with a set of carefully chosen orthogonal polynomials and is used to extract the low level texture features present in the image under analysis. The orthogonal polynomials model coefficients are reordered into multiresolution subband like structure. Simple statistical and perceptual properties are derived from the subband coefficients to represent the texture features and these features form a feature vector. The efficiency of the proposed feature vector extraction for texture image retrieval is experimented on the standard Brodatz and MIT's VisTex texture database images with the Canberra distance measure. The proposed method is compared with other existing retrieval schemes such as Discrete Cosine Transformation (DCT) based multiresolution subbands, Gabor wavelet and Contourlet Transform based retrieval schemes and is found to outperform the existing schemes with less computational cost.