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Detection of Landscape in Images
Dufka, Zbyněk ; Křupka, Aleš (referee) ; Číka, Petr (advisor)
The target of this thesis is theoretical summary of methods, which are used for detection of objects in database of images. Next step is to develope own algorhytm for detection of landscape. Theoretical part describes methods Viola-Jones and HOG, which are used for detection of human faces and anomalies on radiographs. This analysis is concerned with segmentation method and algorhytm for data mining from images, which contain the landscape scenery. Appropriate method and its analysis could contribute to create and to implement into developement enviroment RapidMiner in the second part of thesis.
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Image Stabilization
Ohrádka, Marek ; Beneš, Radek (referee) ; Číka, Petr (advisor)
This thesis deals with digital image stabilization. It contains a brief overview of the problem and available methods for digital image stabilization. The aim was to design and implement image stabilization system in JAVA, which is designed for RapidMiner. Two new stabilization methods have been proposed. The first is based on the motion estimation and motion compensation using Full-search and Three-step search algorithms. The basis of the second method is the detection of object boundaries. The functionality of the proposed method was tested on video sequences with contain visible shake of the scene, which has beed created for this purpose. Testing results show that with the proper set of input parameters for the object border detection method, successful stabilization of the scene is achieved. The rate of error reduction between images is approximately about 65 to 85%. The output of the method is stabilized image sequence and a set of metadata collected during stabilization, which can be further processed in an environment of RapidMiner.
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Data Mining
Stehno, David ; Hynčica, Tomáš (referee) ; Honzík, Petr (advisor)
The aim of the thesis was to study and describe data mining methodology CRISP-DM. From the collected database of calls to the call center a prediction was performed, based on CRISP-DM methodology. In phase of test situation modeling four different testing methods were used: the k-NN, neural network, linear regression and super vector machine. The input attributes importance for further prediction was evaluated based on different selections. The results and findings may provide data for further more accurate forecasts in the future; not only in number of calls but also other indicators relevant to the call center.
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GRID Aided RapidMiner
Mikulín, Ondřej ; Kučera, Pavel (referee) ; Honzík, Petr (advisor)
The aim of this work is integration of RapidMiner into the GRID environment and parallelization of mathematical model optimization. Different subtask time complexity was considered during realization. Result of this work is application RapidParallel based on GPL software.
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Musical genre classification
Káčerová, Erika ; Říha, Kamil (referee) ; Uher, Václav (advisor)
The aim of this bachelor thesis is creating a system for automatic music genre recognition. The thesis deals with two main issues, which are feature extraction of a genre and machine learning process. For the purpose of feature extraction a source code is written in JAVA programming language based on jAudio library. Six machine learning models are created in RapidMiner Studio software. The most appropriate one of them, Neural Networks method is then improved and tested on different parts of songs from database.These database contains 250 training songs and 25 test songs from five music genres: classical music, disco, drum and bass, hip hop and rock.
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Intelligent Mailbox
Pohlídal, Antonín ; Drozd, Michal (referee) ; Chmelař, Petr (advisor)
This master's thesis deals with the use of text classification for sorting of incoming emails. First, there is described the Knowledge Discovery in Databases and there is also analyzed in detail the text classification with selected methods. Further, this thesis describes the email communication and SMTP, POP3 and IMAP protocols. The next part contains design of the system that classifies incoming emails and there are also described realated technologie ie Apache James Server, PostgreSQL and RapidMiner. Further, there is described the implementation of all necessary components. The last part contains an experiments with email server using Enron Dataset.
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Classification Framework
Koroncziová, Dominika ; Otrusina, Lubomír (referee) ; Kouřil, Jan (advisor)
The goal of this work is the design and implementation of a machine learning software, based on the RapidMiner library. The finished application integrates the most commonly used algorithms and processes implemented in RapidMiner into an easily usable program. The application contains a simple command line interface, as well as a graphic interface to simplify selection of multiple parameters. The program also provides a tool to create standalone programs, that can be used for classification with a pre-trained model. On top of the original requirements the possibility to work with textual data from Wikipedia was also implemented, providing a tool for downloading and preprocessing of the data in order to use them as training input. This text focuses on the specifics of the algorithms and classifiers used and on their features and uses, and describes the design and implementation of the system. As part of this work, several tests were run in order to validate the efficiency and functionality of the program. The test results are included at the end of the thesis.
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Knowledge Discovery in Multimedia Databases
Jirmásek, Tomáš ; Řezníček, Ivo (referee) ; Chmelař, Petr (advisor)
This master's thesis deals with knowledge discovery in databases, especially basic methods of classification and prediction used for data mining are described here. The next chapter contains introduction to multimedia databases and knowledge discovery in multimedia databases. The main goal of this chapter was to focus on extraction of low level features from video data and images. In the next parts of this work, there is described data set and results of experiments in applications RapidMiner, LibSVM and own developed application. The last chapter summarises results of used methods for high level feature extraction from low level description of data.
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Automatic image screenshots retrieval from video data using JAVA platform
Kulhavý, Miloslav ; Říha, Kamil (referee) ; Burget, Radim (advisor)
This thesis deals with automatic detection of transition scenes videos on the JAVA platform. It was created experiment, which recognizes the scene transitions in video samples and evaluates the recognition accuracy. For the realization of the experiment was created 512 video samples (256 with and 256 without scenes transitions), each of the seven screenshots. These samples were analyzed and by decision tree classified into one of two classes, depending on where they contain a transition of scenes or not. For it was used RapidMiner tool, and its extensions VIMI and IMMI. The purpose of this thesis is train the automatic detection of scenes transition and find the optimal settings of a decision tree for the highest classification accuracy. Highest accuracy was 75.2 %.
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Digital Image Noise Reduction Methods
Čišecký, Roman ; Říha, Kamil (referee) ; Číka, Petr (advisor)
The master's thesis is concerned with digital image denoising methods. The theoretical part explains some elementary terms related to image processing, image noise, categorization of noise and quality determining criteria of denoising process. There are also particular denoising methods described, mentioning their advantages and disadvantages in this paper. The practical part deals with an implementation of the selected denoising methods in a Java, in the environment of application RapidMiner. In conclusion, the results obtained by different methods are compared.
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