Fast Features Invariant to Rotation and Scale of Texture

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00218863" target="_blank" >RIV/68407700:21230/14:00218863 - isvavai.cz</a>

Výsledek na webu

<a href="http://cmp.felk.cvut.cz/~sulcmila/papers/Sulc-TR-2014-12.pdf" target="_blank" >http://cmp.felk.cvut.cz/~sulcmila/papers/Sulc-TR-2014-12.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Fast Features Invariant to Rotation and Scale of Texture

Popis výsledku v původním jazyce

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 fullset 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.

Název v anglickém jazyce

Fast Features Invariant to Rotation and Scale of Texture

Popis výsledku anglicky

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 fullset 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.

Druh

V_souhrn - Souhrnná výzkumná zpráva

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Počet stran výsledku

20

Místo vydání

Praha

Název nakladatele resp. objednatele

Center for Machine Perception, K13133 FEE Czech Technical University

Verze

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