National Repository of Grey Literature 58 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
Presentation interface for analytical module of the Videolytics system
Ďurišková, Dominika ; Skopal, Tomáš (advisor) ; Lokoč, Jakub (referee)
With the sharp increase in the number of surveillance cameras in public spaces in recent years, there is a rapidly increasing need for video processing and analysis without the necessity of human assistance. Computers are able to process several times more information in much less time than humans. In addition, thanks to the impressive progress in the field of machine learning algorithms and artificial intelligence, computer-based video analysis is becoming a common part of everyday life and is steadily finding its way in various fields. In this thesis, we design and implement a graphical user interface for the analytical module of the Videolytics system. We aim to design a graphical user interface that is user-friendly, simple and at the same time allows users to enter visual queries and modify query parameters. The presentation part is focused on the process and logic of working with the results and their rendering. Additionally, it also allows the export of this data for further processing by external applications and the import of the post-processed data. Finally, we show the module in practice and its ways of application in practical life on the enclosed examples. 1
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.
Tree-based indexing methods for similarity search in metric and nonmetric spaces
Lokoč, Jakub
Title: Tree-based Indexing Methods for Similarity Search in Metric and Nonmetric Spaces Author: Mgr. Jakub Lokoč Department: Department of Software Engineering Faculty of Mathematics and Physics Charles University in Prague Supervisor: Doc. RNDr. Tomáš Skopal, Ph.D. Author's e-mail address: Supervisor's e-mail address: Abstract: The M-tree is a well-known indexing method enabling efficient similarity search in metric spaces. Although the M-tree is an aging method nowadays, we believe it still offers an undiscovered potential. We present sev- eral approaches and directions that show how the original M-tree algorithms and structure can be improved. To allow more efficient query processing by the M-tree, we propose several new methods of (parallel) M-tree construction that achieve more compact M-tree hierarchies and preserve acceptable con- struction cost. We also demonstrate that the M-tree can be simply extended to a new indexing method - the NM-tree, which allows efficient nonmetric similarity search by use of the TriGen algorithm. All these experimentally verified improvements show that the M-tree can still be regarded as an im- portant dynamic metric access method suitable for management of large collections of unstructured data. Moreover, all the improvements can be...
Multi-model Approach For Effective Multimedia Exploration
Grošup, Tomáš ; Lokoč, Jakub (advisor)
This work is focusing on exploration of multimedia collections. It describes the problematic of exploration and proposes new approaches to it, two based on the data structure M-Index and two utilizing multiple similarity models at once. Those approaches were compared using an extensive user study. Part of this work is also devoted to analysis of a new exploration system, design of its architecture, system implementation and its deployment. This exploration system was used in several applications, which are also shown and described in this thesis.
Re-identification of Objects in Video Stream using Data Analytics
Smrž, Dominik ; Skopal, Tomáš (advisor) ; Lokoč, Jakub (referee)
The wide usage of surveillance cameras provides data that can be used in various areas, such as security and urban planning. An important stepping stone for useful information extraction is matching the seen object across different points in time or different cameras. In this work, we focus specifically on this part of the video processing, usually referred to as re-identification. We split our work into two stages. In the first part, we focus on the spatial and temporal information regarding the detected objects. In the second part, we combine this metadata with the visual information. For the extraction of useful descriptors from the images, we use methods based on the color distribution as well as state-of-the-art deep neural networks. We also annotate a dataset to provide a comprehensive evaluation of our approaches. Additionally, we provide a custom tool we used to annotate the dataset. 1
Detekce střihů a vyhledávání známých scén ve videu s pomocí metod hlubokého učení
Souček, Tomáš ; Lokoč, Jakub (advisor) ; Peška, Ladislav (referee)
Video retrieval represents a challenging problem with many caveats and sub-problems. This thesis focuses on two of these sub-problems, namely shot transition detection and text-based search. In the case of shot detection, many solutions have been proposed over the last decades. Recently, deep learning-based approaches improved the accuracy of shot transition detection using 3D convolutional architectures and artificially created training data, but one hundred percent accuracy is still an unreachable ideal. In this thesis we present a deep network for shot transition detection TransNet V2 that reaches state-of- the-art performance on respected benchmarks. In the second case of text-based search, deep learning models projecting textual query and video frames into a joint space proved to be effective for text-based video retrieval. We investigate these query representation learning models in a setting of known-item search and propose improvements for the text encoding part of the model. 1
Searching Image Collections Using Deep Representations of Local Regions
Bátoryová, Jana ; Lokoč, Jakub (advisor) ; Fink, Jiří (referee)
In a known-item search task (KIS), the goal is to find a previously seen image in a multimedia collection. In this thesis, we discuss two different approaches based on the visual description of the image. In the first one, the user creates a collage of images (using images from an external search engine), based on which we provide the most similar results from the dataset. Our results show that preprocessing the images in the dataset by splitting them into several parts is a better way to work with the spatial information contained in the user input. We compared the approach to a baseline, which does not utilize this spatial information and an approach that alters a layer in a deep neural network. We also present an alternative approach to the KIS task, search by faces. In this approach, we work with the faces extracted from the images. We investigate face representation for the ability to sort the faces based on their similarity. Then we present a structure that allows easy exploration of the set of faces. We provide a demo, implementing all presented techniques.
Known-item search with relevance to SOM feedback
Veselý, Patrik ; Lokoč, Jakub (advisor) ; Vomlelová, Marta (referee)
Multimedia searching is usually realized by means of text search, where a large dataset is sorted with respect to a relevance to a given text query. However, if users search for just one scene or image, a sequential browsing of a larger result set is often necessary, without a guarantee that the object is found in a reasonable time. This work focuses on methods relying on relevance feedback for more effective searching in a large collection of one million images. Several relevance update and display selection approaches are compared using simulations of relevance feedback. Our experiments reveal that the investigated models are a benefit to modern multimedia search engines. 1
Automatic recognition of musical notation from audio data
Čermák, Marek ; Lokoč, Jakub (advisor) ; Hajič, Jan (referee)
Title: Automatic recognition of musical notation from audio data Author: Marek Čermák Department: Department of Software Engineering Supervisor: doc. RNDr. Jakub Lokoč, Ph.D. Abstract: The goal of this thesis is the design and implementation of an application using convolutional neural networks to generate musical notation from audio data. The application is able to train a neural network using input files in the MIDI (Musical Instrument Digital Interface) format and pair all sections of the music with their audio form. The training of the neural network can be performed on a user- specified collection of MIDI files or on randomly generated music. Each instrument in the MIDI standard can be assigned a network whose output are the notes playing in the given time section. Continuously iterating over the audio data, the network generates sections of active notes which are then concatenated into the output file. The application is also capable of recognizing words from audio using an external service. Keywords: musical notation, neural network, deep learning, audio recognition, MIDI
Evaluation of Keyword-Based Search Models for Known-Item Search
Mejzlík, František ; Lokoč, Jakub (advisor) ; Skopal, Tomáš (referee)
Video retrieval over large datasets is still a very challenging task, which is getting even more relevant with the rapidly growing volume of unannotated data available. Know-item search, as one of the video retrieval tasks, is limited primarily due to the limited ability of users to formulate a suitable query and low efectivity of search models. This thesis focuses mainly on selected search models based on image classifcation, which we will also compare with a commercial solution. We will examine how to transform the network output and what models to use. Also, the efect of iterative user query reformulation on overall search efectivity will be investigated. We will also present a simple simulated user model for the generation of artifcial queries and supporting software for data collection and model evaluation in a web interface. 1

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