Fast Features Invariant to Rotation and Scale of Texture

被引:4
|
作者
Sulc, Milan [1 ]
Matas, Jiri [1 ]
机构
[1] Czech Tech Univ, Ctr Machine Percept, Fac Elect Engn, Dept Cybernet, CR-16635 Prague, Czech Republic
关键词
Texture; Classification; LBP; LBP-HF; Histogram; SVM; Feature maps; Ffirst; LOCAL BINARY PATTERNS; CLASSIFICATION;
D O I
10.1007/978-3-319-16181-5_4
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A family of novel texture representations called Ffirst, the Fast Features Invariant to Rotation and Scale of Texture, is introduced. New rotation invariants are proposed, extending the LBP-HF features, improving the recognition accuracy. Using the full set of LBP features, as opposed to uniform only, leads to further improvement. Linear Support Vector Machines with an approximate chi(2) -kernel map are used for fast and precise classification. Experimental results show that Ffirst exceeds the best reported results in texture classification on three difficult texture datasets KTH-TIPS2a, KTH-TIPS2b and ALOT, achieving 88 %, 76 % and 96 % accuracy respectively. The recognition rates are above 99 % on standard texture datasets KTH-TIPS, Brodatz32, UIUCTex, UMD, CUReT.
引用
收藏
页码:47 / 62
页数:16
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