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Multimedia Document Type Diff
Lang, Jozef ; Hlosta, Martin (referee) ; Chmelař, Petr (advisor)
Development of Internet and its massive spread resulted in increased volume of multimedia data. The increase in the amount of multimedia data raises the need for efficient similarity detection between multimedia files for the purpose of preventing and detecting violations of copyright licenses or for detection of similar or duplicate files. This thesis discusses the current options in the field of the content-based image and video comparison and focuses on the feature extraction techniques, distance metrics, design and implementation of the mediaDiff application module for the content-based comparison of video files.
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MediaDiff - Diff for Static Images
Brothánek, Jan ; Mlích, Jozef (referee) ; Chmelař, Petr (advisor)
This bachelor's thesis focuses on comparison of digital documents, especially static images. This paper presents algorithms, which are usable for general data comparison and also specific algorithms major in image data comparison. We introduce possibilities of document comparison which are now available for users. On the basis of this current situation, a prototype of new modular application MediaDiff for various documents comparison is designed and implemented. We describe in detail each stage of the application development, problems which had arisen and their solutions. The possibilities of future development of the application are discussed.
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Evolutionary Algorithms for Data Transformation
Švec, Ondřej ; Pilát, Martin (advisor) ; Neruda, Roman (referee)
In this work, we propose a novel method for a supervised dimensionality reduc- tion, which learns weights of a neural network using an evolutionary algorithm, CMA-ES, optimising the success rate of the k-NN classifier. If no activation func- tions are used in the neural network, the algorithm essentially performs a linear transformation, which can also be used inside of the Mahalanobis distance. There- fore our method can be considered to be a metric learning algorithm. By adding activations to the neural network, the algorithm can learn non-linear transfor- mations as well. We consider reductions to low-dimensional spaces, which are useful for data visualisation, and demonstrate that the resulting projections pro- vide better performance than other dimensionality reduction techniques and also that the visualisations provide better distinctions between the classes in the data thanks to the locality of the k-NN classifier. 1
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MediaDiff - Diff for Static Images
Brothánek, Jan ; Mlích, Jozef (referee) ; Chmelař, Petr (advisor)
This bachelor's thesis focuses on comparison of digital documents, especially static images. This paper presents algorithms, which are usable for general data comparison and also specific algorithms major in image data comparison. We introduce possibilities of document comparison which are now available for users. On the basis of this current situation, a prototype of new modular application MediaDiff for various documents comparison is designed and implemented. We describe in detail each stage of the application development, problems which had arisen and their solutions. The possibilities of future development of the application are discussed.
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Multimedia Document Type Diff
Lang, Jozef ; Hlosta, Martin (referee) ; Chmelař, Petr (advisor)
Development of Internet and its massive spread resulted in increased volume of multimedia data. The increase in the amount of multimedia data raises the need for efficient similarity detection between multimedia files for the purpose of preventing and detecting violations of copyright licenses or for detection of similar or duplicate files. This thesis discusses the current options in the field of the content-based image and video comparison and focuses on the feature extraction techniques, distance metrics, design and implementation of the mediaDiff application module for the content-based comparison of video files.
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