Multi-resolution fourier-based texture image retrieval
Proceedings of the 2009 conference on Information Science, Technology and Applications
Intelligent Processing of Medical Images in the Wavelet Domain
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
IEEE Transactions on Neural Networks
Sign and magnitude of local patterns for image indexing and retrieval
International Journal of Computational Vision and Robotics
A relevance feedback-based learner for image retrieval using SIFT descriptors
International Journal of Computational Vision and Robotics
Expert system design using wavelet and color vocabulary trees for image retrieval
Expert Systems with Applications: An International Journal
Rotation Invariant Curvelet Features for Region Based Image Retrieval
International Journal of Computer Vision
Directional line edge binary pattern for texture image indexing and retrieval
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Content-based texture image retrieval using fuzzy class membership
Pattern Recognition Letters
Modified color motif co-occurrence matrix for image indexing and retrieval
Computers and Electrical Engineering
Single tree species classification from Terrestrial Laser Scanning data for forest inventory
Pattern Recognition Letters
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This paper proposes a novel approach for rotation-invariant texture image retrieval by using set of dual-tree rotated complex wavelet filter (DT-RCWF) and DT complex wavelet transform (DT-CWT) jointly, which obtains texture features in 12 different directions. Two-dimensional RCWFs are nonseparable and oriented, which improves characterization of oriented textures. Robust and efficient isotropic rotationally invariant features are extracted from DT-RCWF and DT-CWT decomposed subbands. This paper demonstrates the effectiveness of this new set of features on four different sets of rotated and nonrotated databases. Experimental results indicate that the proposed method improves retrieval accuracy from 83.17% to 93.71% on a small size (208 images) nonrotated database D1, from 82.71% to 90.86% on a small size (208 images) rotated database D2, from 72.18% to 76.09% on a medium-size (640 images) rotated database D3, and from 64.17% to 78.93% on a large size (1856 images) rotated database D4, compared with the discrete wavelet transform-based approach. New method also retains complexity