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Feature Extraction on Local Jet Space for Texture Classification

Abstract : The proposal of this study is to analyze the texture pattern recognition over the local jet space looking forward to improve the texture characterization. Local jets decompose the image based on partial derivatives allowing the texture feature extraction be exploited in different levels of geometrical structures. Each local jet component evidences a different local pattern, such as, flat regions, directional variations and concavity or convexity. Subsequently , a texture descriptor is used to extract features from 0th, 1st and 2nd-derivative components. Four well-known databases (Brodatz, Vistex, Usptex and Outex) and four texture descriptors (Fourier descriptors, Gabor filters, Local Binary Pattern and Local Binary Pattern Variance) were used to validate the idea, showing in most cases an increase of the success rates.
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Submitted on : Wednesday, December 16, 2015 - 4:47:49 PM
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Marcos William da Silva Oliveira, Núbia Rosa da Silva, Antoine Manzanera, Odemir Martinez Bruno. Feature Extraction on Local Jet Space for Texture Classification. Physica A: Statistical Mechanics and its Applications, Elsevier, 2015, ⟨10.1016/j.physa.2015.06.046⟩. ⟨hal-01245094⟩

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