Reference TypeConference Proceedings
Citation KeyMontoya-ZegarraLeitTorr:2007:RoScSt
Author1 Montoya-Zegarra, Javier Alexander
2 Leite, Neucimar J.
3 Torres da Silva, Ricardo
Affiliation1 Institute of Computing, State University of Campinas
2 Institute of Computing, State University of Campinas
3 Institute of Computing, State University of Campinas
TitleRotation-Invariant and Scale-Invariant Steerable Pyramid Decomposition for Texture Image Retrieval
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 20 (SIBGRAPI)
EditorFalcão, Alexandre Xavier
Lopes, Hélio Côrtes Vieira
Book TitleProceedings
DateOct. 7-10, 2007
Publisher CityLos Alamitos
PublisherIEEE Computer Society
Conference LocationBelo Horizonte
KeywordsSteerable Pyramid Decomposition, Texture, Content-Based Image Retrieval, Texture-based Image Retrieval, Feature Extraction.
AbstractThis 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 characteriztion based on Gabor Wavelets with regard to their retrieval effectiveness. Results demonstrate the superiority of the proposed method in rotated and scaled image datasets.
Tertiary TypeFull Paper
FormatPrinted, On-line.
Size892 KiB
Number of Files1
Target Filemontoya.zegarra-RotInvSclInvTexImgRet.pdf
Last Update2007: administrator
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