Rotation-invariant and scale-invariant steerable pyramid decomposition for texture image retrieval
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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 recent 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. © 2007 IEEE.
Computational geometry , Computer graphics , Database systems , Digital image storage , Image coding , Image retrieval , Imaging systems , Information retrieval , Rotation; Textures , Brodatz , Dominant orientations , Feature elements , Image data sets , Image subbands , Invariant representations , Retrieval effectivenesses , Scale invariances , Standard deviations , Steerable pyramids , Texture characterizations