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Knowledge Discovery from Web Logs
Valaštín, Samuel ; Rychlý, Marek (referee) ; Bartík, Vladimír (advisor)
This bachelor thesis deals with the problem of knowledge discovery from web logs. The data source in the form of web access logs allows, after appropriate preprocessing, the use of a number of techniques that are designed to deal with knowledge discovery. By applying these techniques to preprocessed data, it is possible to classify user behavior into groups, to discover interesting associations in user behavior, or to discover previously unknown sequences in common user behavior.
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Neural Network Based Image Segmentation
Vrábelová, Pavla ; Žák, Pavel (referee) ; Švub, Miroslav (advisor)
This paper deals with application of neural networks in image segmentation. First part is an introduction to image processing and neural networks, second part describes an implementation of segmentation system and presents results of experiments. The segmentation system enables to use different types of classifiers, various image features extraction and also to evaluate the success of segmentation. Two classifiers were created - a neural network (self-organizing map) and an algorithm K-means. Colour (RGB and HSV) and texture features and their combinations were used for classification. Texture features were extracted using a set of Gabor filters. Experiments with designed classifiers and feature extractors were carried out and results were compared.
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Object Detection and Tracking Using Interest Points
Bílý, Vojtěch ; Hradiš, Michal (referee) ; Juránek, Roman (advisor)
This paper deals with object detection and tracking using iterest points. Existing approaches are described here. Inovated method based on Generalized Hough transform and iterative Hough-space searching is proposed in this paper. Generality of proposed detector is shown in various types of objects. Object tracking is designed as frame by frame detection.
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Unsupervised Evaluation of Speaker Recognition System
Odehnal, Ondřej ; Plchot, Oldřich (referee) ; Matějka, Pavel (advisor)
Tato práce je vystavěna nad moderním systémem pro rozpoznávání mluvčího (SID) založeného na x-vektorech. Cílem bakalářské práce je navrhnout a experimentálně vyhodnotit techniky pro evaluaci SID systému za použití audio nahrávek bez anotace tj. bez znalosti mluvčího. Pro tento účel je z každé nahrávky bez anotace vytvořen embedding. Ty se poté používají pro shlukování nahrávek a následné vytvoření pseudo-anotací. Na těchto anotacích se SID systém evaluuje pomocí equal error rate (EER) metriky. Za účelem vytvoření pseudo-anotací byly navrženy tyto shlukovací algoritmy učení bez učitele: K-means, Gaussian mixture models (GMM) a aglomerativní shlukování. Po testování vyšel jakožto nejlepší experimentální postup K-means se Silhouette metrikou, která používá kosinovou podobnost jako míru vzdálenosti. Nejlepší metoda dosáhla 5,72 % EER s referenčním EER = 5,15 %, které bylo spočítané se znalostí anotace na části datasetu SITW dev-core-core. Podobné výsledky byly získány na části datasetu SITW eval-core-core s odhadnutým EER = 5,86 % a referenčním 5,08 %. Rozdíl mezi hodnotami tvoří 0,57 % pro eval-core-core a 0, 78% pro dev-core-core. Další testy na NIST SRE16 a VoxCeleb1 datasetech byly provedeny za účelem ověření správnosti navrženého postupu. Obecně se dá říct, že navržený testovací postup měl chybu přibližně 1 %, což je poměrně dobrý výsledek pro algoritmus učení bez učitele.
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Demonstrational Program for IZU Course
Míšová, Miroslava ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This bachelor's thesis deals with development of new study aplications for course Fundamentals of Artificial Intelligence. These aplications are based on the older version of JavaApplet, which use features, that are no longer supported. Each applicatoin was made acording to an object-oriented paradigm and than implemented. Special care was taken in order for the UI to be intuitive and easy to use and also for the aplication to be able to be further developed.
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Systems for remote measurement in power engineering
Hudec, Lukáš ; Mlýnek, Petr (referee) ; Mišurec, Jiří (advisor)
The work deals with the measurement and management in power. Provides an introduction to the problems of remote meter reading, management, and describes the current situation in the field of modern technologies Smart metering and Smart grids. It also analyzed the issue of collection of networks and data collection from a large number of meters over a wide area. For the purpose of data transmission are described GPRS, PLC, DSL, ... Further, there are given options to streamline communication. This area is used hierarchical aggregation. Using k-means algorithm is a program designed to calculate the number of concentrators and their location in the group of meters. The finished program is written in Java. It has a graphical interface and shows how the calculation is conducted. To verify the results of the optimization program is given simulation model in OPNET Modeler tool. Audited results are described in the conclusion and can deduce that using the optimization program is to streamline communications.
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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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Image Database Query by Example
Dobrotka, Matúš ; Hradiš, Michal (referee) ; Veľas, Martin (advisor)
This thesis deals with content-based image retrieval. The objective of the thesis is to develop an application, which will compare different approaches of image retrieval. First basic approach consists of keypoints detection, local features extraction and creating a visual vocabulary by clustering algorithm - k-means. Using this visual vocabulary is computed histogram of occurrence count of visual words - Bag of Words (BoW), which globally represents an image. After applying an appropriate metrics, it follows finding similar images. Second approach uses deep convolutional neural networks (DCNN) to extract feature vectors. These vectors are used to create a visual vocabulary, which is used to calculate BoW. Next procedure is then similar to the first approach. Third approach uses extracted vectors from DCNN as BoW vectors. It is followed by applying an appropriate metrics and finding similar images. The conclusion describes mentioned approaches, experiments and the final evaluation.
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A Classification Methods for Retinal Nerve Fibre Layer Analysis
Zapletal, Petr ; Kolář, Radim (referee) ; Odstrčilík, Jan (advisor)
This thesis is deal with classification for retinal nerve fibre layer. Texture features from six texture analysis methods are used for classification. All methods calculate feature vector from inputs images. This feature vector is characterized for every cluster (class). Classification is realized by three supervised learning algorithms and one unsupervised learning algorithm. The first testing algorithm is called Ho-Kashyap. The next is Bayess classifier NDDF (Normal Density Discriminant Function). The third is the Nearest Neighbor algorithm k-NN and the last tested classifier is algorithm K-means, which belongs to clustering. For better compactness of this thesis, three methods for selection of training patterns in supervised learning algorithms are implemented. The methods are based on Repeated Random Subsampling Cross Validation, K-Fold Cross Validation and Leave One Out Cross Validation algorithms. All algorithms are quantitatively compared in the sense of classication error evaluation.
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Statistical characteristics of the traffic flow microstructure
Apeltauer, Jiří ; Nagy,, Ivan (referee) ; Kumpošt,, Petr (referee) ; Holcner, Petr (advisor)
The actual traffic flow theory assumes interactions only between neighbouring vehicles within the traffic. This assumption is reasonable, but it is based on the possibilities of science and technology available decades ago, which are currently overcome. Obviously, in general, there is an interaction between vehicles at greater distances (or between multiple vehicles), but at the time, no procedure has been put forward to quantify the distance of this interaction. This work introdukce a method, which use mathematical statistics and precise measurement of time distances of individual vehicles, which allows to determine these interacting distances (between several vehicles) and its validation for narrow densities of traffic flow. It has been revealed that at high traffic flow densities there is an interaction between at least three consecutive vehicles and four and five vehicles at lower densities. Results could be applied in the development of new traffic flow models and its verification.
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