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Title |
Steerable Pyramid Decomposition ? Rotation & Scale invariant texture image retrieval |
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Author |
G.Pandiselvi, V.Umamaheswari, S.Pethammal Jothi, C.Balasubramanian |
| Citation |
Vol. 16 No. 3 pp. 114-118 |
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Abstract |
A new rotation- invariant and scale invariant representation for texture image retrieval process on steerable pyramid decomposition. To obtain rotation or scale invariance, the feature elements are aligned by considering either the dominant orientation or dominant scale of the input textures. Initially, take a various train images (data samples) then extract the various features from that rotational texture images and stored in data base. Similarly test the images, then extract the features of text images and compare with data base based similarity features we can extract image (similar) from the data base. In test Experiments were conducted on the broad 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 maximum similarity images are extracted from the data base and conclude the image retrieval application using feature extraction basis. |
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Keywords |
Texture classification, feature extraction, steerability, rotation invariance, Gabor wavelet. |
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