Rotation-Invariant and Scale-Invariant Steerable Pyramid Decomposition for Texture Image Retrieval
Javier A. Montoya‐Zegarra, Neucimar J. Leite, Ricardo da Silva Torres · 2007
This paper proposes a new rotation-invariant and scale-invariant representation for texture image retrieval based on steerable pyramid decomposition. By calculating the mean and standard deviation of decomposed image subbands, the texture feature vectors are extracted. To obtain rotation or scale invariance, the feature elements are aligned by considering either the dominant orientation or dominant scale of the input textures. Experiments were conducted on the Brodatz database aiming to compare our approach to the conventional steerable pyramid decomposition, and a proposal for texture characterization based on Gabor wavelets with regard to their retrieval effectiveness. Results demonstrate the superiority of the proposed method in rotated and scaled image datasets.