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dc.contributor.authorZhang, Kaitaien_US
dc.contributor.authorChen, Hong-Shuoen_US
dc.contributor.authorWang, Yeen_US
dc.contributor.authorJi, Xiangyangen_US
dc.contributor.authorKuo, C. -C. Jayen_US
dc.date.accessioned2020-05-05T00:02:00Z-
dc.date.available2020-05-05T00:02:00Z-
dc.date.issued2019-01-01en_US
dc.identifier.isbn978-1-5386-6249-6en_US
dc.identifier.issn1522-4880en_US
dc.identifier.urihttp://hdl.handle.net/11536/154053-
dc.description.abstractA hierarchical spatial-spectral correlation (HSSC) method is proposed for texture analysis in this work. The HSSC method first applies a multi-stage spatial-spectral transform to input texture patches, which is known as the Saak transform. Then, it conducts a correlation analysis on Saak transform coefficients to obtain texture features of high discriminant power. To demonstrate the effectiveness of the HSSC method, we conduct extensive experiments on texture classification and show that it offers very competitive results comparing with state-of-the-art methods.en_US
dc.language.isoen_USen_US
dc.subjectTexture analysisen_US
dc.subjecttexture classificationen_US
dc.subjectspatial-spectral transformen_US
dc.subjectspatial-spectral correlationen_US
dc.subjectneural-network-inspired image transformen_US
dc.titleTEXTURE ANALYSIS VIA HIERARCHICAL SPATIAL-SPECTRAL CORRELATION (HSSC)en_US
dc.typeProceedings Paperen_US
dc.identifier.journal2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)en_US
dc.citation.spage4419en_US
dc.citation.epage4423en_US
dc.contributor.department電機學院zh_TW
dc.contributor.departmentCollege of Electrical and Computer Engineeringen_US
dc.identifier.wosnumberWOS:000521828604102en_US
dc.citation.woscount0en_US
Appears in Collections:Conferences Paper