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27 results on '"Budikova, Petra"'

1. Visual Exploration of Human Motion Data

Author

Budikova, Petra, Klepac, Daniel, Rusnak, David, Slovak, Milan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Skopal, Tomáš, editor, Falchi, Fabrizio, editor, Lokoč, Jakub, editor, Sapino, Maria Luisa, editor, Bartolini, Ilaria, editor, and Patella, Marco, editor

Published
2022
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2. Motion Words: A Text-Like Representation of 3D Skeleton Sequences

Author

Sedmidubsky, Jan, Budikova, Petra, Dohnal, Vlastislav, Zezula, Pavel, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jose, Joemon M., editor, Yilmaz, Emine, editor, Magalhães, João, editor, Castells, Pablo, editor, Ferro, Nicola, editor, Silva, Mário J., editor, and Martins, Flávio, editor

Published
2020
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4. Visual Concept Ontology for Image Annotations

Author

Botorek, Jan, Budikova, Petra, and Zezula, Pavel

Subjects
Computer Science - Information Retrieval, Computer Science - Digital Libraries, I.2.4
Abstract

In spite of the development of content-based data management, text-based searching remains the primary means of multimedia retrieval in many areas. Automatic creation of text metadata is thus a crucial tool for increasing the findability of multimedia objects. Search-based annotation tools try to provide content-descriptive keywords by exploiting web data, which are easily available but unstructured and noisy. Such data need to be analyzed with the help of semantic resources that provide knowledge about objects and relationships in a given domain. In this paper, we focus on the task of general-purpose image annotation and present the VCO, a new ontology of visual concepts developed as a part of image annotation framework. The ontology is linked with the WordNet lexical database, so the annotation tools can easily integrate information from both these resources.

Published
2014

5. DISA at ImageCLEF 2014 Revised: Search-based Image Annotation with DeCAF Features

Author

Budikova, Petra, Botorek, Jan, Batko, Michal, and Zezula, Pavel

Subjects
Computer Science - Information Retrieval, Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper constitutes an extension to the report on DISA-MU team participation in the ImageCLEF 2014 Scalable Concept Image Annotation Task as published in [3]. Specifically, we introduce a new similarity search component that was implemented into the system, report on the results achieved by utilizing this component, and analyze the influence of different similarity search parameters on the annotation quality.

Published
2014

6. Query Language for Complex Similarity Queries

Author

Budikova, Petra, Batko, Michal, and Zezula, Pavel

Subjects
Computer Science - Databases, Computer Science - Information Retrieval, Computer Science - Multimedia
Abstract

For complex data types such as multimedia, traditional data management methods are not suitable. Instead of attribute matching approaches, access methods based on object similarity are becoming popular. Recently, this resulted in an intensive research of indexing and searching methods for the similarity-based retrieval. Nowadays, many efficient methods are already available, but using them to build an actual search system still requires specialists that tune the methods and build the system manually. Several attempts have already been made to provide a more convenient high-level interface in a form of query languages for such systems, but these are limited to support only basic similarity queries. In this paper, we propose a new language that allows to formulate content-based queries in a flexible way, taking into account the functionality offered by a particular search engine in use. To ensure this, the language is based on a general data model with an abstract set of operations. Consequently, the language supports various advanced query operations such as similarity joins, reverse nearest neighbor queries, or distinct kNN queries, as well as multi-object and multi-modal queries. The language is primarily designed to be used with the MESSIF framework for content-based searching but can be employed by other retrieval systems as well.

Published
2012

7. Fusion Strategies for Large-Scale Multi-modal Image Retrieval

Author

Budikova, Petra, Batko, Michal, Zezula, Pavel, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Hameurlain, Abdelkader, editor, Küng, Josef, editor, Wagner, Roland, editor, Akbarinia, Reza, editor, and Pacitti, Esther, editor

Published
2017
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8. Search-Based Image Annotation: Extracting Semantics from Similar Images

Author

Budikova, Petra, Batko, Michal, Botorek, Jan, Zezula, Pavel, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Mothe, Josanne, editor, Savoy, Jacques, editor, Kamps, Jaap, editor, Pinel-Sauvagnat, Karen, editor, Jones, Gareth, editor, San Juan, Eric, editor, Capellato, Linda, editor, and Ferro, Nicola, editor

Published
2015
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10. Similarity Query Postprocessing by Ranking

Author

Budikova, Petra, Batko, Michal, Zezula, Pavel, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Detyniecki, Marcin, editor, Knees, Peter, editor, Nürnberger, Andreas, editor, Schedl, Markus, editor, and Stober, Sebastian, editor

Published
2011
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11. Evaluation Platform for Content-Based Image Retrieval Systems

Author

Budikova, Petra, Batko, Michal, Zezula, Pavel, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Gradmann, Stefan, editor, Borri, Francesca, editor, Meghini, Carlo, editor, and Schuldt, Heiko, editor

Published
2011
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21. Inherent Fusion

Author

Budikova, Petra, primary, Batko, Michal, additional, Novak, David, additional, and Zezula, Pavel, additional

Published
2016
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24. Inherent Fusion: Towards Scalable Multi-Modal Similarity Search.

Author

Budikova, Petra, Batko, Michal, Novak, David, and Zezula, Pavel

Subjects
BIG data, MULTISENSOR data fusion, MODAL analysis, MULTIMEDIA systems, QUERY (Information retrieval system)
Abstract

The rapid growth of unstructured data, commonly denoted as the Big Data challenge, requires new technologies that are capable of dealing with complex data objects such as multimedia. In this work, the authors focus on the content-based retrieval approach, which is able to organize such data by exploiting the similarity of data content. In particular, they focus on solutions that are able to combine multiple similarity measures during the query evaluation. The authors introduce a classification of existing approaches and analyze their performance in terms of effectiveness, efficiency, and scalability. Further, they present a novel technique of inherent fusion that combines the efficiency of fast indexed retrieval with the effectiveness of ranking methods. The performance of all discussed methods is evaluated by extensive experiments with user participation. [ABSTRACT FROM AUTHOR]

Published
2016
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26. Inherent Fusion: Towards Scalable Multi-Modal Similarity Search

Author

Novak, David, Zezula, Pavel, Budikova, Petra, and Batko, Michal

Abstract

The rapid growth of unstructured data, commonly denoted as the Big Data challenge, requires new technologies that are capable of dealing with complex data objects such as multimedia. In this work, the authors focus on the content-based retrieval approach, which is able to organize such data by exploiting the similarity of data content. In particular, they focus on solutions that are able to combine multiple similarity measures during the query evaluation. The authors introduce a classification of existing approaches and analyze their performance in terms of effectiveness, efficiency, and scalability. Further, they present a novel technique of inherent fusion that combines the efficiency of fast indexed retrieval with the effectiveness of ranking methods. The performance of all discussed methods is evaluated by extensive experiments with user participation.

Published
2016
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