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Automatic Machine Learning Methods for Multimedia Data Analysis
Mašek, Jan ; Chromý, Erik (referee) ; Vozňák, Miroslav (referee) ; Burget, Radim (advisor)
The quality and efficient processing of increasing amount of multimedia data is nowadays becoming increasingly needed to obtain some knowledge of this data. The thesis deals with a research, implementation, optimization and the experimental verification of automatic machine learning methods for multimedia data analysis. Created approach achieves higher accuracy in comparison with common methods, when applied on selected examples. Selected results were published in journals with impact factor [1, 2]. For these reasons special parallel computing methods were created in this work. These methods use massively parallel hardware to save electric energy and computing time and for achieving better result while solving problems. Computations which usually take days can be computed in minutes using new optimized methods. The functionality of created methods was verified on selected problems: artery detection from ultrasound images with further classifying of artery disease, the buildings detection from aerial images for obtaining geographical coordinates, the detection of materials contained in meteorite from CT images, the processing of huge databases of structured data, the classification of metallurgical materials with using laser induced breakdown spectroscopy and the automatic classification of emotions from texts.
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Algortihm Optimization Using SIMD Instructions
Sedláček, Marek ; Rydlo, Štěpán (referee) ; Orság, Filip (advisor)
This thesis talks about techniques which can be used to optimize run time of algorithms. For a demonstration of these techniques algorithms from different fields were chosen, namely particle swarm optimization, circle drawing algorithm and image (matrix) rotation algorithm. These algorithms were written in Python 3, C language and assembly language using SIMD instructions. While writing these codes emphases was placed on code efficiency. These practices were in this thesis described and compared, same as the impact on algorithm optimization. Performed tests upheld expected potential of SIMD technology for optimization, but also that this approach cannot be used in all cases. In case of circle drawing the SIMD approach achieved more than ten times better speeds than the serial implementation in C and more than one thousand times better speed than Python 3 implementation. In case of particle swarm optimization the result was opposite -- serial C implementation achieved a better speed than SIMD implementation.
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Algortihm Optimization Using SIMD Instructions
Sedláček, Marek ; Rydlo, Štěpán (referee) ; Orság, Filip (advisor)
This thesis talks about techniques which can be used to optimize run time of algorithms. For a demonstration of these techniques algorithms from different fields were chosen, namely particle swarm optimization, circle drawing algorithm and image (matrix) rotation algorithm. These algorithms were written in Python 3, C language and assembly language using SIMD instructions. While writing these codes emphases was placed on code efficiency. These practices were in this thesis described and compared, same as the impact on algorithm optimization. Performed tests upheld expected potential of SIMD technology for optimization, but also that this approach cannot be used in all cases. In case of circle drawing the SIMD approach achieved more than ten times better speeds than the serial implementation in C and more than one thousand times better speed than Python 3 implementation. In case of particle swarm optimization the result was opposite -- serial C implementation achieved a better speed than SIMD implementation.
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Automatic Machine Learning Methods for Multimedia Data Analysis
Mašek, Jan ; Chromý, Erik (referee) ; Vozňák, Miroslav (referee) ; Burget, Radim (advisor)
The quality and efficient processing of increasing amount of multimedia data is nowadays becoming increasingly needed to obtain some knowledge of this data. The thesis deals with a research, implementation, optimization and the experimental verification of automatic machine learning methods for multimedia data analysis. Created approach achieves higher accuracy in comparison with common methods, when applied on selected examples. Selected results were published in journals with impact factor [1, 2]. For these reasons special parallel computing methods were created in this work. These methods use massively parallel hardware to save electric energy and computing time and for achieving better result while solving problems. Computations which usually take days can be computed in minutes using new optimized methods. The functionality of created methods was verified on selected problems: artery detection from ultrasound images with further classifying of artery disease, the buildings detection from aerial images for obtaining geographical coordinates, the detection of materials contained in meteorite from CT images, the processing of huge databases of structured data, the classification of metallurgical materials with using laser induced breakdown spectroscopy and the automatic classification of emotions from texts.
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