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|Title:||Learning how to extract rotation-invariant and scale-invariant features from texture images|
|Authors:||Montoya Zegarra, Javier|
Paulo Papa, Joao
da Silva Torres, Ricardo
|Keywords:||Classification (of information);Computer networks;Image enhancement;Rotation;Textures;Brodatz;Classification rates;Data sets;Discriminating power;Distorted images;Feature vector (FV);image descriptor;Invariant features;multiclass recognition;rotation invariant;Steerable pyramid (SP);Texture features;texture images;Texture recognition;Feature extraction|
|Abstract:||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.|
|Appears in Collections:||Artículos de investigación|
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