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\ThesisAuthor{Bc. Vojtěch Dorňák}

\ThesisSupervisor{Ing. Lukáš Pospíšil, Ph.D.}

\CzechThesisTitle{Algoritmy pro klasifikaci visuálních signálů s využitím technik extrakce významných rysů}

\EnglishThesisTitle{Algorithms used for classifying visual signals using methods for extracting significant features}

\SubmissionYear{2021}

\Acknowledgement{I would like thank the thesis supervisor Ing. Lukáš Pospíšil, Ph.D. and the thesis consultant Ing. Marek Pecha for their guidance throughout every step of writing this thesis. Their expertise helped form this text into the version you are reading now. I would also like to thank my girlfriend Nikola Viková for her everyday support while this thesis was being created. }

\CzechAbstract{Klasifikace vizuálních dat je důležitý úkol v moderním oboru počítačového vidění. Kvalita často používaných neuronových sítí závisí na velkém objemu trénovacích dat. Tato práce prozkoumává alternativní konvekční klasifikační metody, kde je rozměr dat redukován pomocí projekce na prostor významných bodů. Lokální a globální techniky extrakce významných bodů, a to SIFT, SURF, ORB a PCA jsou porovnány. Klasifikace pomocí SVM a Bayeovský Model je prozkoumána na třech datasetech o malém množství trénovacích dat a různé komplexnosti.}

\CzechKeywords{klasifikace vizuálních dat; extrakce rysů; Support Vector Machine; Bag of Words; k-means; Scale-Invariant Feature Transform; Speed-Up Robust Features; Oriented Fast and Rotated Brief; Analýza hlavních komponent}

\EnglishAbstract{Classification of visual data is a fundamental task in state-of-the-art computer vision. The efficiency of the commonly used neural networks crucially depends on a large amount of training data. This thesis explores alternative conventional classification methods, where the dimension of the visual data is reduced by projecting the data onto a space of significant features. The local and global feature extraction techniques, namely SIFT, SURF, ORB, and PCA are compared. The classification by SVM and the Bayesian Model is examined on three datasets with a small number of training images of different complexity.}

\EnglishKeywords{visual data classification; feature extraction; Support Vector Machine; Bag of Words; k-means; Scale-Invariant Feature Transform; Speed-Up Robust Features; Oriented Fast and Rotated Brief; Principal Component Analysis}

\AddAcronym{SIFT}{Scale Invariant Feature Transform}
\AddAcronym{DoG}{Difference of Gaussian}
\AddAcronym{SURF}{Speed Up Robust Features}
\AddAcronym{ORB}{Oriented Fast and Rotated Brief}
\AddAcronym{SVM}{Support Vector Machine}
\AddAcronym{PCA}{Principal Component Analysis}
\AddAcronym{BoVW}{Bag of Visual Words}

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\include{content/introduction/introduction}

\include{content/extract/extract}

\include{content/classifiers/classifiers}

\include{content/data/data}

\include{content/results/results}

\include{content/conclusion/conclusion}

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