Sparse representation and image classification with the shearlet transform

Examensarbete för masterexamen

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dc.contributor.authorAndersson, Robin
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.departmentChalmers University of Technology / Department of Mathematical Sciencesen
dc.date.accessioned2019-07-03T14:37:48Z-
dc.date.available2019-07-03T14:37:48Z-
dc.date.issued2017
dc.identifier.urihttps://hdl.handle.net/20.500.12380/251854-
dc.description.abstractClassical 2D-wavelet transforms have suboptimal compression performance due to its inability to generate sparse representation of discontinuities along lines. This thesis contains investigations of the shearlet transform which in contrast to classical 2D-wavelet transforms is directional. The shearlet transform has optimal compression performance of so called "cartoon-like images" and performs better than wavelet on real images too. Besides image compression the thesis concerns image classification using the shearlet transform as a component of the feature extraction procedure. Images are transformed to symmetric and positive definite (SPD) matrices. The space of SPD matrices is not a linear space but is on the other hand a Riemannian manifold with the structure that provides. For the classification task, a kernel support vector classifier is used that uses the log-Euclidean metric on the space of SPD matrices. The thesis was written at Syntronic Software Innovations.
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectGrundläggande vetenskaper
dc.subjectMatematik
dc.subjectBasic Sciences
dc.subjectMathematics
dc.titleSparse representation and image classification with the shearlet transform
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
Collection:Examensarbeten för masterexamen // Master Theses



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