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TRECVid Search Information Retrieval
Čeloud, David ; Mlích, Jozef (referee) ; Chmelař, Petr (advisor)
The master's thesis deals with Information Retrieval. It summarizes the knowledge in the field of Information Retrieval theory. Furthermore, the work gives an overview of models used in Information Retrieval, the data and the actual issues and their possible solutions. The practical part of the master's thesis is focused on the implementation of methods of information retrieval in textual data. The last part is dedicated to experiments validating the implementation and its possible improvements.
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Knowledge Discovery in Multimedia Databases
Málik, Peter ; Bartík, Vladimír (referee) ; Chmelař, Petr (advisor)
This master"s thesis deals with the knowledge discovery in multimedia databases. It contains general principles of knowledge discovery in databases, especially methods of cluster analysis used for data mining in large and multidimensional databases are described here. The next chapter contains introduction to multimedia databases, focusing on the extraction of low level features from images and video data. The practical part is then an implementation of the methods BIRCH, DBSCAN and k-means for cluster analysis. Final part is dedicated to experiments above TRECVid 2008 dataset and description of achievements.
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Knowledge Discovery in Image Databases
Jaroš, Ondřej ; Řezníček, Ivo (referee) ; Chmelař, Petr (advisor)
This thesis is focused on knowledge discovery from databases, especially on methods of classification and prediction. These methods are described in detail. Furthermore, this work deals with multimedia databases and the way these databases store data. In particular, the method for processing low-level image and video data is described. The practical part of the thesis focuses on the implementation of this GMM method used for extracting low-level features of video data and images. In other parts, input data and tools, which the implemented method was compared with, are described. The last section focuses on experiments comparing extraction efficiency features of high-level attributes of low-level data and the methods implemented in selected classification tools LibSVM.
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Knowledge Discovery in Multimedia Databases
Jurčák, Petr ; Řezníček, Ivo (referee) ; Chmelař, Petr (advisor)
This master's thesis is dedicated to theme of knowledge discovery in Multimedia Databases, especially basic methods of classification and prediction used for data mining. The other part described about extraction of low level features from video data and images and summarizes information about content-based search in multimedia content and indexing this type of data. Final part is dedicated to implementation Gaussian mixtures model for classification and compare the final result with other method SVM.
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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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Information Retrieval
Šabatka, Pavel ; Bartík, Vladimír (referee) ; Chmelař, Petr (advisor)
The purpose of this thesis is a summary of theoretical knowledge in the field of information retrieval. This document contains mathematical models that can be used for information retrieval algorithms, including how to rank them. There are also examined the specifics of image and text data. The practical part is then an implementation of the algorithm in video shots of the TRECVid 2009 dataset based on high-level features. The uniqueness of this algorithm is to use internet search engines to obtain terms similarity. The work contains a detailed description of the implemented algorithm including the process of tuning and conclusions of its testing.
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TRECVid Search Information Retrieval
Čeloud, David ; Mlích, Jozef (referee) ; Chmelař, Petr (advisor)
The master's thesis deals with Information Retrieval. It summarizes the knowledge in the field of Information Retrieval theory. Furthermore, the work gives an overview of models used in Information Retrieval, the data and the actual issues and their possible solutions. The practical part of the master's thesis is focused on the implementation of methods of information retrieval in textual data. The last part is dedicated to experiments validating the implementation and its possible improvements.
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Information Retrieval
Šabatka, Pavel ; Bartík, Vladimír (referee) ; Chmelař, Petr (advisor)
The purpose of this thesis is a summary of theoretical knowledge in the field of information retrieval. This document contains mathematical models that can be used for information retrieval algorithms, including how to rank them. There are also examined the specifics of image and text data. The practical part is then an implementation of the algorithm in video shots of the TRECVid 2009 dataset based on high-level features. The uniqueness of this algorithm is to use internet search engines to obtain terms similarity. The work contains a detailed description of the implemented algorithm including the process of tuning and conclusions of its testing.
|
|
|
Knowledge Discovery in Image Databases
Jaroš, Ondřej ; Řezníček, Ivo (referee) ; Chmelař, Petr (advisor)
This thesis is focused on knowledge discovery from databases, especially on methods of classification and prediction. These methods are described in detail. Furthermore, this work deals with multimedia databases and the way these databases store data. In particular, the method for processing low-level image and video data is described. The practical part of the thesis focuses on the implementation of this GMM method used for extracting low-level features of video data and images. In other parts, input data and tools, which the implemented method was compared with, are described. The last section focuses on experiments comparing extraction efficiency features of high-level attributes of low-level data and the methods implemented in selected classification tools LibSVM.
|
|
|
Knowledge Discovery in Multimedia Databases
Jurčák, Petr ; Řezníček, Ivo (referee) ; Chmelař, Petr (advisor)
This master's thesis is dedicated to theme of knowledge discovery in Multimedia Databases, especially basic methods of classification and prediction used for data mining. The other part described about extraction of low level features from video data and images and summarizes information about content-based search in multimedia content and indexing this type of data. Final part is dedicated to implementation Gaussian mixtures model for classification and compare the final result with other method SVM.
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