About: Kernel-mapped Histograms of Multi-scale LBPs for Tree Bark Recognition

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	We propose a novel method for tree bark identification by SVM classification of feature-mapped multi-scale descriptors formed by concatenated histograms of Local Binary Patterns (LBPs). A feature map approximating the histogram intersection kernel significantly improves the methods accuracy. Contrary to common practice, we use the full 256 bin LBP histogram rather than the standard 59 bin histogram of uniform LBPs and obtain superior results. Robustness to scale changes is handled by forming multiple multi-scale descriptors. Experiments conducted on a standard dataset show 96.5% accuracy using ten-fold cross validation. Using the standard 15 training examples per class, the proposed method achieves a recognition rate of 82.5% and significantly outperforms both the state-of-the-art automatic recognition rate of 64.2% and human experts with recognition rates of 56.6% and 77.8%. Experiments on standard texture datasets confirm that the proposed method is suitable for general texture recognition. We propose a novel method for tree bark identification by SVM classification of feature-mapped multi-scale descriptors formed by concatenated histograms of Local Binary Patterns (LBPs). A feature map approximating the histogram intersection kernel significantly improves the methods accuracy. Contrary to common practice, we use the full 256 bin LBP histogram rather than the standard 59 bin histogram of uniform LBPs and obtain superior results. Robustness to scale changes is handled by forming multiple multi-scale descriptors. Experiments conducted on a standard dataset show 96.5% accuracy using ten-fold cross validation. Using the standard 15 training examples per class, the proposed method achieves a recognition rate of 82.5% and significantly outperforms both the state-of-the-art automatic recognition rate of 64.2% and human experts with recognition rates of 56.6% and 77.8%. Experiments on standard texture datasets confirm that the proposed method is suitable for general texture recognition. (en)
Title	Kernel-mapped Histograms of Multi-scale LBPs for Tree Bark Recognition Kernel-mapped Histograms of Multi-scale LBPs for Tree Bark Recognition (en)
skos:prefLabel	Kernel-mapped Histograms of Multi-scale LBPs for Tree Bark Recognition Kernel-mapped Histograms of Multi-scale LBPs for Tree Bark Recognition (en)
skos:notation	RIV/68407700:21230/13:00211358!RIV15-MSM-21230___
http://linked.open...avai/riv/aktivita	P S
http://linked.open...avai/riv/aktivity	P(GBP103/12/G084), P(TE01020415), S
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Šulc, Milan Matas, Jiří
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	82609
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/13:00211358
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	texture recognition; bark; plant identification; LBP (en)
http://linked.open.../riv/klicoveSlovo	texture recognition LBP bark plant identification
http://linked.open...ontrolniKodProRIV	[032C2F386D7F]
http://linked.open...v/mistoKonaniAkce	Wellington
http://linked.open...i/riv/mistoVydani	Piscataway
http://linked.open...i/riv/nazevZdroje	28th International Conference of Image and Vision Computing New Zealand (IVCNZ 2013)
http://linked.open...in/vavai/riv/obor	JD
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...vavai/riv/projekt	Center for Large Scale Multi-modal Data Interpretation V3C - Visual Computing Competence Center
http://linked.open...UplatneniVysledku	2013
http://linked.open...iv/tvurceVysledku	Matas, Jiří Šulc, Milan
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2013-11-27 (xsd:date)
issn	2151-2191
number of pages	6 (xsd:int)
http://bibframe.org/vocab/doi	10.1109/IVCNZ.2013.6726996
http://purl.org/ne...btex#hasPublisher	IEEE
https://schema.org/isbn	978-1-4799-0882-0
http://localhost/t...ganizacniJednotka	21230

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