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Comparison of signature-based and semantic similarity models
Kovalčík, Gregor ; Lokoč, Jakub (advisor) ; Mráz, František (referee)
Content-based image retrieval and similarity search has been investigated for several decades with many different approaches proposed. This thesis fo- cuses on a comparison of two orthogonal similarity models on two different im- age retrieval tasks. More specifically, traditional image representation models based on feature signatures are compared with models based on state-of-the-art deep convolutional neural networks. Query-by-example benchmarking and tar- get browsing tasks were selected for the comparison. In a thorough experimental evaluation, we confirm that models based on deep convolutional neural networks outperform the traditional models. However, in the target browsing scenario, we show that the traditional models could still represent an effective option. We have also implemented a feature signature extractor into the OpenCV library in order to make the source codes available for the image retrieval and computer vision community. 1
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Effective visualization for interactive video exploration
Pavlovský, Jan ; Lokoč, Jakub (advisor) ; Grošup, Tomáš (referee)
In this thesis we introduce an innovative approach to visualisation and search results presentation for large video collection search and browsing. The general problem of video search is analysed and discussed in comparison with other current software tools and methods used for video search. A specific visualisa- tion method and algorithm for its generation is then proposed and discussed. We evaluated the methods both, empirically and by a user study. Based on the results, we chose the best possible algorithm settings for interactive video search and applied them. A simple experimental software tool implementing the proposed methods is developed focusing on the visualisation components. 1
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Aplikace umělých neuronových sítí pro detekci malware v HTTPS komunikaci
Bodnár, Jan ; Lokoč, Jakub (advisor) ; Somol, Petr (referee)
A huge proportion of modern malicious software uses Internet connec- tions. Therefore, it is possible to detect infected computers by inspecting network activity. Since attackers hide the content of communication by com- municating over encrypted protocols such as HTTPS, communication must be analysed purely on the basis of metadata. Cisco provided us a dataset containing aggregated metadata with additional information as to whether or not each sample contains malicious communication. This work trains neu- ral networks to distinguish between infected and benign samples, comparing different architectures of neural networks and providing a comparison with results achieved by different machine learning methods tried by colleagues. It also seeks to create a mapping which maps samples of communication into a space where different samples of malicious communication created by a sin- gle malware family form clusters. This may make it easier to find different computers infected by a virus with known behaviour, even when the virus cannot be detected by the detection system. 1
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Efficient kNN classification of malware from HTTPS data
Maroušek, Jakub ; Lokoč, Jakub (advisor) ; Galamboš, Leo (referee)
An important task of Network Intrusion Detection Systems (NIDS) is to detect malign com- munication in a computer network traffic. The traditional detection approaches which analyze the content of network packets, are becoming insufficient with an increased usage of encrypted HTTPS protocol. The previous research shows, however, that the high-level properties of HTTPS commu- nication such as the duration of a request or the number of bytes sent/received from the client to the server may be successfully used to detect behavioral patterns of malware activity. We study approximate k-NN similarity joins as one of the methods to build a classifier recognizing malign communication. Three MapReduce-based and one centralized approximate k-NN join methods are reimplemented in order to support large volumes of high-dimensional data. Finally, we thoroughly evaluate all methods on different datasets containing vectors up to 1000 dimensions and compare multiple aspects concerning scalability, approximation precision and classification precision of each approach.
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Using Metric Indexes For Effective and Efficient Multimedia Exploration
Čech, Přemysl ; Lokoč, Jakub (advisor)
The exponential growth of multimedia data challenges effectiveness and efficiency of the state-of-the- art retrieval techniques. In this thesis, we focus on browsing of large datasets using exploration approaches where query cannot be particularly expressed or where the need of some general notion of the dataset is required. More specifically, we study exploration scenarios utilizing the metric access method PM-Tree which natively creates a hierarchy of nested metric regions. We enhance the PM- Tree for the exploration purposes and define different traversing and querying strategies. Further, we investigate range multi-query (range query defined by multiple objects) approaches. We propose new effective and efficient cut-region range query and compare the new query with other approaches for efficient multi-query processing. Finally, we implemented all new methods and strategies for the PM- Tree into multimedia exploration framework and tested browsing algorithms on a game-like demo application.
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Object recognition using 3D convolutional neural networks
Moravec, Jaroslav ; Lokoč, Jakub (advisor) ; Straka, Milan (referee)
Title: Object recognition using 3D convolutional neural networks Author: Jaroslav Moravec Department: Department of Software Engineering Supervisor: RNDr. Jakub Lokoč, Ph.D., Department of Software Engineering Abstract: With the fast development of laser and sensor technologies, it has become easy to scan a real-world object and save it in a digital format into a persistent database. With the rising number of scanned 3D objects, data man- agement and retrieval methods become necessary. For various retrieval tasks, effective retrieval models are required. In our work, we focus on effective classifi- cation and similarity search. The investigated approach is based on convolutional neural networks representing a machine learning method that boomed in recent years. We have designed and trained several architectures of 3D convolutional neural networks and tested them on state-of-the-art benchmark 3D datasets for 3D object recognition and retrieval. We were also able to show that the trained features on one dataset can be then used to predict class labels on another 3D dataset. Keywords: Object recognition, 3D convolution, neural networks
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Detection of malignant melanoma in histological sample using deep neural networks
Frey, Adam ; Lokoč, Jakub (advisor) ; Straka, Milan (referee)
The aim of this thesis is to create a classification method for detection of ma- lignant melanoma in high-resolution digital images. Deep convolutional neural networks were used for this task. At first, a short overview of malignant melanoma and ways to detect it is presented. Deep convolutional neural networks are also introduced with a special attention given to models used further in this work. Several ways to generate samples from the provided histological images are discussed, and several experiments are evaluated to decide how to maximize the accuracy of employed classification methods. The thesis then focuses on several neural network structures used for image classification and their possible utiliza- tion for the given task. The emphasis is laid on the transfer learning, a method used for modifying already trained models for different tasks. This method is then used for training several classifiers. Further on, several methods for the visualization of model results are discussed with some of them implemented. The experiments show promising results on par with other studies dealing with similar problems. Several possibilities for further development are listed in the conclusion.
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Efficient video retrieval using complex sketches and exploration based on semantic descriptors
Blažek, Adam ; Lokoč, Jakub (advisor) ; Mráz, František (referee)
This thesis focuses on novel video retrieval scenarios. More particularly, we aim at the Known-item Search scenario wherein users search for a short video segment known either visually or by a textual description. The scenario assumes that there is no ideal query example available. Our former known- item search tool relying on color feature signatures is extended with major enhancements. Namely, we introduce a multi-modal sketching tool, the exploration of video content with semantic descriptors derived from deep convolutional networks, new browsing/visualization methods and two orthogonal approaches for textual search. The proposed approaches are embodied in our video retrieval tool Enhanced Sketch-based Video Browser (ESBVB). To evaluate ESBVB performance, we participated in international competitions comparing our tool with the state-of-the-art approaches. Repeatedly, our tool outperformed the other methods. Furthermore, we show in our user study that even novice users are able to effectively employ ESBVB capabilities to search and browse known video clips. Powered by TCPDF (www.tcpdf.org)
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Vektorový screencast
Rozsíval, Šimon ; Děcký, Martin (advisor) ; Lokoč, Jakub (referee)
The goal of this bachelor thesis is to create a software for recording and playback of educational videos for Khanova škola (Czech clone of Khan Academy). Contrary to common videos the visual data is not stored as a sequence of bitmaps, but as vectors. This allows to reduce the data bandwidth and playback sharp images in any target resolution. The player and also the tool for recording the videos runs in a web browser. The thesis also focuses on designing and implementing a suitable file format for storing the visual and audio data and implementing the software according to the client/server paradigm. Powered by TCPDF (www.tcpdf.org)
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The Impact of Image Resolution on the Precision of Content-based Retrieval
Navrátil, Lukáš ; Lokoč, Jakub (advisor) ; Skopal, Tomáš (referee)
This thesis is focused on comparing methods for similarity image retrieval. Common techniques and testing sets are introduced. The testing sets are there to measure the accuracy of the searching systems based on similarity image retrieval. Measurements are done on those models which are implemented on the basis of presented techniques. These measurements examine their results depending on the input data, used components and parameters settings, especially the impact of image resolution on the retrieval precision is examined. These results are analysed and the models are compared. Powered by TCPDF (www.tcpdf.org)
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