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| rdf:type
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| Description
| - A frequently used strategy for road sign classification is based on the normalized cross-correlation similarity to class prototypes followed by the nearest neighbor classifier. Because of the global nature of the cross-correlation similarity, this method suffers from presence of uninformative pixels (caused e.g. by occlusions), and is computationally demanding. In this paper, a novel concept of a trainable similarity measure is introduced which alleviates these shortcomings. The similarity is based on individual matches in a set of local image regions. The set of regions, relevant for a particular similarity assessment, is refined by the training process. It is illustrated on a set of experiments with road sign classification problems that the trainable similarity yields high-performance data representations and classifiers. Apart from a multi-class classification accuracy, also non-sign rejection capability, and computational demands in execution are discussed. It appears that the trainable simi...
- A frequently used strategy for road sign classification is based on the normalized cross-correlation similarity to class prototypes followed by the nearest neighbor classifier. Because of the global nature of the cross-correlation similarity, this method suffers from presence of uninformative pixels (caused e.g. by occlusions), and is computationally demanding. In this paper, a novel concept of a trainable similarity measure is introduced which alleviates these shortcomings. The similarity is based on individual matches in a set of local image regions. The set of regions, relevant for a particular similarity assessment, is refined by the training process. It is illustrated on a set of experiments with road sign classification problems that the trainable similarity yields high-performance data representations and classifiers. Apart from a multi-class classification accuracy, also non-sign rejection capability, and computational demands in execution are discussed. It appears that the trainable simi... (en)
- Návrh klasifikátoru dopravních značek založeného na podobnosti zkoumaného objektu ke třídě značek reprezentované vždy typickou značkou (prototype-based rule). Navržen algoritmus založený na trénování podle množiny prototypů (trainable similarity). Experimenty na několika datových souborech ilustrují vyšší účinnost klasifikátoru v porovnání s klasifikátory až dosud používanými pro klasifikaci značek. Byly testovány i další vlastnosti navrženého klasifikátoru jako robustnost a časová náročnost. (cs)
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| Title
| - Building Road-Sign Classifiers Using a Trainable Similarity Measure
- Building Road-Sign Classifiers Using a Trainable Similarity Measure (en)
- Klasifikace dopravních značek založená na míře podobnosti zkoumaného objektu k třídě reprezentované typickou značkou (cs)
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| skos:prefLabel
| - Building Road-Sign Classifiers Using a Trainable Similarity Measure
- Building Road-Sign Classifiers Using a Trainable Similarity Measure (en)
- Klasifikace dopravních značek založená na míře podobnosti zkoumaného objektu k třídě reprezentované typickou značkou (cs)
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| skos:notation
| - RIV/67985556:_____/06:00041079!RIV07-AV0-67985556
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| http://linked.open.../vavai/riv/strany
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
| - P(2C06019), P(IAA2075302), R, Z(AV0Z10750506)
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| http://linked.open...iv/cisloPeriodika
| |
| http://linked.open...vai/riv/dodaniDat
| |
| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
| |
| http://linked.open...iv/duvernostUdaju
| |
| http://linked.open...titaPredkladatele
| |
| http://linked.open...dnocenehoVysledku
| |
| http://linked.open...ai/riv/idVysledku
| - RIV/67985556:_____/06:00041079
|
| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - classifier system design; road-sign classification; similarity data representation (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...odStatuVydavatele
| - US - Spojené státy americké
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...i/riv/nazevZdroje
| - IEEE Transactions on Intelligent Transportation Systems
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| http://linked.open...in/vavai/riv/obor
| |
| http://linked.open...ichTvurcuVysledku
| |
| http://linked.open...cetTvurcuVysledku
| |
| http://linked.open...vavai/riv/projekt
| |
| http://linked.open...UplatneniVysledku
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| http://linked.open...v/svazekPeriodika
| |
| http://linked.open...iv/tvurceVysledku
| - Novovičová, Jana
- Duin, R. P. W.
- Paclík, P.
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| http://linked.open...n/vavai/riv/zamer
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| issn
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| number of pages
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| is http://linked.open...avai/riv/vysledek
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