id: 05700962
dt: j
an: 05700962
au: Montoya-Zegarra, Javier A.; Papa, João Paulo; Leite, Neucimar J.; da Silva
    Torres, Ricardo; Falcão, Alexandre X.
ti: Learning how to extract rotation-invariant and scale-invariant features
    from texture images.
so: EURASIP J. Adv. Signal Process. 2008, Article ID 691924, 15 p. (2008).
py: 2008
pu: Springer International Publishing, Basel
la: EN
cc: 
ut: 
ci: 
li: doi:10.1155/2008/691924
ab: Summary: Learning how to extract texture features from noncontrolled
    environments characterized by distorted images is a still-open task. By
    using a new rotation-invariant and scale-invariant image descriptor
    based on steerable pyramid decomposition, and a novel multiclass
    recognition method based on optimum-path forest, a new texture
    recognition system is proposed. By combining the discriminating power
    of our image descriptor and classifier, our system uses small-size
    feature vectors to characterize texture images without compromising
    overall classification rates. State-of-the-art recognition results are
    further presented on the Brodatz data set. High classification rates
    demonstrate the superiority of the proposed system.
rv: