Open AccessPLT‐based spectral features for texture image retrieval
Author(s) -
Sana Joydeb Kumar,
Islam Md. Monirul
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5604
Subject(s) - artificial intelligence , pattern recognition (psychology) , curvelet , computer science , image texture , gabor filter , gaussian , computer vision , texture (cosmology) , feature (linguistics) , feature extraction , image retrieval , distortion (music) , image (mathematics) , image processing , wavelet transform , wavelet , bandwidth (computing) , amplifier , computer network , linguistics , philosophy , physics , quantum mechanics
Effective texture feature is an essential component in any content‐based image retrieval system. In this study, new texture features based on image enhancement technique are presented. The authors have effectively exploited power‐law transform (PLT) to extract new spectral texture features called PLT‐based spectral features. Extensive experiments on the Brodatz texture database and Salzburg Textures image database prove the effectiveness of the proposed techniques and show that the proposed features significantly outperform the widely used Gabor and curvelet features. The proposed features are also compared with recently published Gaussian copula models of Gabor feature and local tetra patterns (LTrP). The experimental results confirm that the proposed features have more tolerance to scale, orientation and illumination distortion than the state‐of‐the‐art Gabor, curvelet, Gaussian copula models of Gabor and LTrPs.