Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhang, Kaitai | en_US |
dc.contributor.author | Chen, Hong-Shuo | en_US |
dc.contributor.author | Wang, Ye | en_US |
dc.contributor.author | Ji, Xiangyang | en_US |
dc.contributor.author | Kuo, C. -C. Jay | en_US |
dc.date.accessioned | 2020-05-05T00:02:00Z | - |
dc.date.available | 2020-05-05T00:02:00Z | - |
dc.date.issued | 2019-01-01 | en_US |
dc.identifier.isbn | 978-1-5386-6249-6 | en_US |
dc.identifier.issn | 1522-4880 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/154053 | - |
dc.description.abstract | A 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.iso | en_US | en_US |
dc.subject | Texture analysis | en_US |
dc.subject | texture classification | en_US |
dc.subject | spatial-spectral transform | en_US |
dc.subject | spatial-spectral correlation | en_US |
dc.subject | neural-network-inspired image transform | en_US |
dc.title | TEXTURE ANALYSIS VIA HIERARCHICAL SPATIAL-SPECTRAL CORRELATION (HSSC) | en_US |
dc.type | Proceedings Paper | en_US |
dc.identifier.journal | 2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | en_US |
dc.citation.spage | 4419 | en_US |
dc.citation.epage | 4423 | en_US |
dc.contributor.department | 電機學院 | zh_TW |
dc.contributor.department | College of Electrical and Computer Engineering | en_US |
dc.identifier.wosnumber | WOS:000521828604102 | en_US |
dc.citation.woscount | 0 | en_US |
Appears in Collections: | Conferences Paper |