Готові списки джерел за темами / Metadata mining

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Добірка наукової літератури з теми "Metadata mining"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 19 лютого 2022

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Статті в журналах з теми "Metadata mining"

Sutton, Stuart A. "Mining the Metadata Quarries." Bulletin of the American Society for Information Science and Technology 29, no. 2 (January 31, 2005): 11. http://dx.doi.org/10.1002/bult.267.

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Illien, Gildas. "Metadata mining : fouiller les données des catalogues ?" Enrichir pour partager, no. 76 (October 1, 2014): 15–16. http://dx.doi.org/10.35562/arabesques.890.

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Şah, Melike, and Vincent Wade. "Automatic metadata mining from multilingual enterprise content." Journal of Web Semantics 11 (March 2012): 41–62. http://dx.doi.org/10.1016/j.websem.2011.11.001.

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LI, G., H. SHENG, and X. FAN. "Incorporating Metadata into Data Mining with Ontology." IEICE Transactions on Information and Systems E90-D, no. 6 (June 1, 2007): 983–85. http://dx.doi.org/10.1093/ietisy/e90-d.6.983.

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Murraças, Adriana, Paula Maria Vaz Martins, Carlos Daniel Cipriani Ferreira, Tiago Marques Godinho, and Augusto Marques Ferreira da Silva. "Data Mining of MR Technical Parameters." International Journal of E-Health and Medical Communications 12, no. 1 (January 2021): 16–33. http://dx.doi.org/10.4018/ijehmc.2021010102.

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Exposure to radiofrequency (RF) energy during a magnetic resonance imaging exam is a safety concern related to biological thermal effects. Estimation of the specific absorption rate (SAR) is done by manufacturer scanner integrated tools to monitor RF energy. This work presents an exploratory approach of DICOM metadata focused in whole-body SAR values, patient dependent parameters, and pulse sequences. Previously acquired abdominopelvic and head studies were retrieved from a 3 Tesla scanner. Dicoogle tool was used for metadata indexing, mining, and extraction. Specifically weighted pulse sequences were related with weight, BMI, and gender through boxplot diagrams and effect size analysis. A decrease of SAR values with increasing body weight and BMI categories is observable for abdominopelvic studies. Head studies showed different trends regarding distinct pulse sequences; in addition, underage patients register higher SAR values compared to adults. Male individuals register marginally higher SAR values. Metadata recording practices and standardization need to be improved.

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Nurandini, Indri, and Arief Fatchul Huda. "Klastering Dokumen dengan Menambahkan Metadata Menggunakan Algoritma COATES." Kubik: Jurnal Publikasi Ilmiah Matematika 2, no. 2 (November 30, 2017): 39–44. http://dx.doi.org/10.15575/kubik.v2i2.1859.

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Text mining adalah proses ekstraksi pola berupa informasi dan pengetahuan yang berguna dari sejumlah besar sumber data tak terstruktur. Salah satu perkembangan text mining adalah ruang lingkup perbaikan dari pemanfaatan sebuah “side information” yang digunakan untuk membantu proses klastering yang lebih efisien. “side information” yang dimiliki data dapat membantu proses text mining jika “side information” tersebut bersifat informatif. Di dalam “side information” , metadata merupakan bagian dari “side information” yang dimiliki oleh data. Oleh karena itu, algoritma klastering partisi klasik dan model probabilistik dalam text mining telah dikembangkan untuk memproses data bersama “side information” dengan menggunakan algoritma Content and Auxiliary attribute Based Text Clustering (COATES). Adapun proses klastering ini menggunakan inisialisasi klaster dengan algoritma k-means berdasarkan perhitungan jarak euclidean distance.

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Wang, Fei Chao. "A Novel Approach to Mine Knowledge from Social Images." Advanced Materials Research 430-432 (January 2012): 1068–71. http://dx.doi.org/10.4028/www.scientific.net/amr.430-432.1068.

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With the popularity of various social media website, currently, lots of social images attached with different kinds of metadata have been uploaded to social media websites. Mining useful knowledge from social images has been an emerging important research topic in web search and data mining. In this paper, we propose a novel approach to find geographical difference of a given concept from social image community. We put a given concept to social image community, and then downloaded social images with metadata, particularly, the place where the photo was taken should be provided in advance. Firstly, concept is submitted to social image community, and then social images with different kinds of metadata are downloaded. Secondly, social images are clustered according to metadata of images. Finally, the information of concept’s geographical difference is found. Experiments conducted on social image community proof the effectiveness of our approach. Keywords: Social Images, Data Mining, Social Image Community, Image Clustering.

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Intagorn, Suradej, and Kristina Lerman. "Mining Geospatial Knowledge on the Social Web." International Journal of Information Systems for Crisis Response and Management 3, no. 2 (April 2011): 33–47. http://dx.doi.org/10.4018/jiscrm.2011040103.

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Up-to-date geospatial information can help crisis management community to coordinate its response. In addition to data that is created and curated by experts, there is an abundance of user-generated, user-curated data on Social Web sites such as Flickr, Twitter, and Google Earth. User-generated data and metadata can be used to harvest knowledge, including geospatial knowledge that will help solve real-world problems including information discovery, geospatial information integration and data management. This paper proposes a method for acquiring geospatial knowledge in the form of places and relations between them from the user-generated data and metadata on the Social Web. The key to acquiring geospatial knowledge from social metadata is the ability to accurately represent places. The authors describe a simple, efficient algorithm for finding a non-convex boundary of a region from a sample of points from that region. Used within a procedure that learns part-of relations between places from real-world data extracted from the social photo-sharing site Flickr, the proposed algorithm leads to more precise relations than the earlier method and helps uncover knowledge not contained in expert-curated geospatial knowledge bases.

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Su, Shian, Vincent J. Carey, Lori Shepherd, Matthew Ritchie, Martin T. Morgan, and Sean Davis. "BiocPkgTools: Toolkit for mining the Bioconductor package ecosystem." F1000Research 8 (May 29, 2019): 752. http://dx.doi.org/10.12688/f1000research.19410.1.

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Motivation: The Bioconductor project, a large collection of open source software for the comprehension of large-scale biological data, continues to grow with new packages added each week, motivating the development of software tools focused on exposing package metadata to developers and users. The resulting BiocPkgTools package facilitates access to extensive metadata in computable form covering the Bioconductor package ecosystem, facilitating downstream applications such as custom reporting, data and text mining of Bioconductor package text descriptions, graph analytics over package dependencies, and custom search approaches. Results: The BiocPkgTools package has been incorporated into the Bioconductor project, installs using standard procedures, and runs on any system supporting R. It provides functions to load detailed package metadata, longitudinal package download statistics, package dependencies, and Bioconductor build reports, all in "tidy data" form. BiocPkgTools can convert from tidy data structures to graph structures, enabling graph-based analytics and visualization. An end-user-friendly graphical package explorer aids in task-centric package discovery. Full documentation and example use cases are included. Availability: The BiocPkgTools software and complete documentation are available from Bioconductor (https://bioconductor.org/packages/BiocPkgTools).

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Algur, Siddu P., and Prashant Bhat. "Web Video Mining: Metadata Predictive Analysis using Classification Techniques." International Journal of Information Technology and Computer Science 8, no. 2 (February 8, 2016): 69–77. http://dx.doi.org/10.5815/ijitcs.2016.02.09.

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Дисертації з теми "Metadata mining"

Demšar, Urška. "Exploring geographical metadata by automatic and visual data mining." Licentiate thesis, KTH, Infrastructure, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-1779.

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Metadata are data about data. They describe characteristicsand content of an original piece of data. Geographical metadatadescribe geospatial data: maps, satellite images and othergeographically referenced material. Such metadata have twocharacteristics, high dimensionality and diversity of attributedata types, which present a problem for traditional data miningalgorithms.Other problems that arise during the exploration ofgeographical metadata are linked to the expertise of the userperforming the analysis. The large amounts of metadata andhundreds of possible attributes limit the exploration for anon-expert user, which results in a potential loss ofinformation that is hidden in metadata.In order to solve some of these problems, this thesispresents an approach for exploration of geographical metadataby a combination of automatic and visual data mining.Visual data mining is a principle that involves the human inthe data exploration by presenting the data in some visualform, allowing the human to get insight into the data and torecognise patterns. The main advantages of visual dataexploration over automatic data mining are that the visualexploration allows a direct interaction with the user, that itis intuitive and does not require complex understanding ofmathematical or statistical algorithms. As a result the userhas a higher confidence in the resulting patterns than if theywere produced by computer only.In the thesis we present the Visual data mining tool (VDMtool), which was developed for exploration of geographicalmetadata for site planning. The tool provides five differentvisualisations: a histogram, a table, a pie chart, a parallelcoordinates visualisation and a clustering visualisation. Thevisualisations are connected using the interactive selectionprinciple called brushing and linking.In the VDM tool the visual data mining concept is integratedwith an automatic data mining method, clustering, which finds ahierarchical structure in the metadata, based on similarity ofmetadata items. In the thesis we present a visualisation of thehierarchical structure in the form of a snowflake graph.Keywords:visualisation, data mining, clustering, treedrawing, geographical metadata.

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Tang, Yaobin. "Butterfly -- A model of provenance." Worcester, Mass. : Worcester Polytechnic Institute, 2009. http://www.wpi.edu/Pubs/ETD/Available/etd-031309-095511/.

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Ramakrishnan, Cartic. "Extracting, Representing and Mining Semantic Metadata from Text: Facilitating Knowledge Discovery in Biomedicine." Wright State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=wright1222021939.

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Dong, Zheng. "Automated Extraction and Retrieval of Metadata by Data Mining : a Case Study of Mining Engine for National Land Survey Sweden." Thesis, University of Gävle, Department of Technology and Built Environment, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-6811.

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Metadata is the important information describing geographical data resources and their key elements. It is used to guarantee the availability and accessibility of the data. ISO 19115 is a metadata standard for geographical information, making the geographical metadata shareable, retrievable, and understandable at the global level. In order to cope with the massive, high-dimensional and high-diversity nature of geographical data, data mining is an applicable method to discover the metadata.This thesis develops and evaluates an automated mining method for extracting metadata from the data environment on the Local Area Network at the National Land Survey of Sweden (NLS). These metadata are prepared and provided across Europe according to the metadata implementing rules for the Infrastructure for Spatial Information in Europe (INSPIRE). The metadata elements are defined according to the numerical formats of four different data entities: document data, time-series data, webpage data, and spatial data. For evaluating the method for further improvement, a few attributes and corresponding metadata of geographical data files are extracted automatically as metadata record in testing, and arranged in database. Based on the extracted metadata schema, a retrieving functionality is used to find the file containing the keyword of metadata user input. In general, the average success rate of metadata extraction and retrieval is 90.0%.The mining engine is developed in C# programming language on top of the database using SQL Server 2005. Lucene.net is also integrated with Visual Studio 2005 to build an indexing framework for extracting and accessing metadata in database.

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Al-Natsheh, Hussein. "Text Mining Approaches for Semantic Similarity Exploration and Metadata Enrichment of Scientific Digital Libraries." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE2062.

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Анотація:

Pour les scientifiques et chercheurs, s’assurer que la connaissance est accessible pour pouvoir être réutilisée et développée est un point crucial. De plus, la façon dont nous stockons et gérons les articles scientifiques et leurs métadonnées dans les bibliothèques numériques détermine la quantité d’articles pertinents que nous pouvons découvrir et auxquels nous pouvons accéder en fonction de la signification réelle d’une requête de recherche. Cependant, sommes-nous en mesure d’explorer tous les documents scientifiques sémantiquement pertinents avec les systèmes existants de recherche d’information au moyen de mots-clés ? Il s’agit là de la question essentielle abordée dans cette thèse. L’objectif principal de nos travaux est d’élargir ou développer le spectre des connaissances des chercheurs travaillant dans un domaine interdisciplinaire lorsqu’ils utilisent les systèmes de recherche d’information des bibliothèques numériques multidisciplinaires. Le problème se pose cependant lorsque de tels chercheurs utilisent des mots-clés de recherche dépendant de la communauté dont ils sont issus alors que d’autres termes scientifiques sont attribués à des concepts pertinents lorsqu’ils sont utilisés dans des communautés de recherche différentes. Afin de proposer une solution à cette tâche d’exploration sémantique dans des bibliothèques numériques multidisciplinaires, nous avons appliqué plusieurs approches de fouille de texte. Tout d’abord, nous avons étudié la représentation sémantique des mots, des phrases, des paragraphes et des documents pour une meilleure estimation de la similarité sémantique. Ensuite, nous avons utilisé les informations sémantiques des mots dans des bases de données lexicales et des graphes de connaissance afin d’améliorer notre approche sémantique. En outre, la thèse présente quelques implémentations de cas d’utilisation du modèle que nous avons proposé For scientists and researchers, it is very critical to ensure knowledge is accessible for re-use and development. Moreover, the way we store and manage scientific articles and their metadata in digital libraries determines the amount of relevant articles we can discover and access depending on what is actually meant in a search query. Yet, are we able to explore all semantically relevant scientific documents with the existing keyword-based search information retrieval systems? This is the primary question addressed in this thesis. Hence, the main purpose of our work is to broaden or expand the knowledge spectrum of researchers working in an interdisciplinary domain when they use the information retrieval systems of multidisciplinary digital libraries. However, the problem raises when such researchers use community-dependent search keywords while other scientific names given to relevant concepts are being used in a different research community.Towards proposing a solution to this semantic exploration task in multidisciplinary digital libraries, we applied several text mining approaches. First, we studied the semantic representation of words, sentences, paragraphs and documents for better semantic similarity estimation. In addition, we utilized the semantic information of words in lexical databases and knowledge graphs in order to enhance our semantic approach. Furthermore, the thesis presents a couple of use-case implementations of our proposed model

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Petersson, Andreas. "Data mining file sharing metadata : A comparison between Random Forests Classificiation and Bayesian Networks." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-11180.

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In this comparative study based on experimentation it is demonstrated that the two evaluated machine learning techniques, Bayesian networks and random forests, have similar predictive power in the domain of classifying torrents on BitTorrent file sharing networks. This work was performed in two steps. First, a literature analysis was performed to gain insight into how the two techniques work and what types of attacks exist against BitTorrent file sharing networks. After the literature analysis, an experiment was performed to evaluate the accuracy of the two techniques. The results show no significant advantage of using one algorithm over the other when only considering accuracy. However, ease of use lies in Random forests’ favour because the technique requires little pre-processing of the data and still generates accurate results with few false positives.

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Petersson, Andreas. "Data mining file sharing metadata : A comparison between Random Forests Classification and Bayesian Networks." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-11285.

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In this comparative study based on experimentation it is demonstrated that the two evaluated machine learning techniques, Bayesian networks and random forests, have similar predictive power in the domain of classifying torrents on BitTorrent file sharing networks.This work was performed in two steps. First, a literature analysis was performed to gain insight into how the two techniques work and what types of attacks exist against BitTorrent file sharing networks. After the literature analysis, an experiment was performed to evaluate the accuracy of the two techniques.The results show no significant advantage of using one algorithm over the other when only considering accuracy. However, ease of use lies in Random forests’ favour because the technique requires little pre-processing of the data and still generates accurate results with few false positives.

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Ferrill, Paul. "REFERENCE DESIGN FOR A SQUADRON LEVEL DATA ARCHIVAL SYSTEM." International Foundation for Telemetering, 2006. http://hdl.handle.net/10150/604259.

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ITC/USA 2006 Conference Proceedings / The Forty-Second Annual International Telemetering Conference and Technical Exhibition / October 23-26, 2006 / Town and Country Resort & Convention Center, San Diego, California As more aircraft are fitted with solid state memory recording systems, the need for a large data archival storage system becomes increasingly important. In addition, there is a need to keep classified and unclassified data separate but available to the aircrews for training and debriefing along with some type of system for cataloging and searching for specific missions. This paper will present a novel approach along with a reference design for using commercially available hardware and software and a minimal amount of custom programming to help address these issues.

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Lockard, Michael T., R. Rajagopalan, and James A. Garling. "MINING IRIG-106 CHAPTER 10 AND HDF-5 DATA." International Foundation for Telemetering, 2006. http://hdl.handle.net/10150/604264.

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ITC/USA 2006 Conference Proceedings / The Forty-Second Annual International Telemetering Conference and Technical Exhibition / October 23-26, 2006 / Town and Country Resort & Convention Center, San Diego, California Rapid access to ever-increasing amounts of test data is becoming a problem. The authors have developed a data-mining methodology solution approach to provide a solution to catalog test files, search metadata attributes to derive test data files of interest, and query test data measurements using a web-based engine to produce results in seconds. Generated graphs allow the user to visualize an overview of the entire test for a selected set of measurements, with areas highlighted where the query conditions were satisfied. The user can then zoom into areas of interest and export selected information.

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Srinivasan, Uma Computer Science &amp Engineering Faculty of Engineering UNSW. "A FRAMEWORK FOR CONCEPTUAL INTEGRATION OF HETEROGENEOUS DATABASES." Awarded by:University of New South Wales. School of Computer Science and Engineering, 1997. http://handle.unsw.edu.au/1959.4/33463.

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Autonomy of operations combined with decentralised management of data has given rise to a number of heterogeneous databases or information systems within an enterprise. These systems are often incompatible in structure as well as content and hence difficult to integrate. This thesis investigates the problem of heterogeneous database integration, in order to meet the increasing demand for obtaining meaningful information from multiple databases without disturbing local autonomy. In spite of heterogeneity, the unity of overall purpose within a common application domain, nevertheless, provides a degree of semantic similarity which manifests itself in the form of similar data structures and common usage patterns of existing information systems. This work introduces a conceptual integration approach that exploits the similarity in meta level information in existing systems and performs metadata mining on database objects to discover a set of concepts common to heterogeneous databases within the same application domain. The conceptual integration approach proposed here utilises the background knowledge available in database structures and usage patterns and generates a set of concepts that serve as a domain abstraction and provide a conceptual layer above existing legacy systems. This conceptual layer is further utilised by an information re-engineering framework that customises and packages information to reflect the unique needs of different user groups within the application domain. The architecture of the information re-engineering framework is based on an object-oriented model that represents the discovered concepts as customised application objects for each distinct user group.

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Книги з теми "Metadata mining"

Sebastian, Nordhoff, Hellmann Sebastian, and SpringerLink (Online service), eds. Linked Data in Linguistics: Representing and Connecting Language Data and Language Metadata. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Multimedia information extraction: Advances in video, audio, and imagery analysis for search, data mining, surveillance, and authoring. Hoboken, N.J: Wiley, 2012.

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García-Barriocanal, Elena. Metadata and Semantic Research: 5th International Conference, MTSR 2011, Izmir, Turkey, October 12-14, 2011. Proceedings. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.

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Sartori, Fabio. Metadata and Semantic Research: Third International Conference, MTSR 2009, Milan, Italy, October 1-2, 2009. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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Sanchez-Alonso, Salvador. Metadata and Semantic Research: 4th International Conference, MTSR 2010, Alcalá de Henares, Spain, October 20-22, 2010. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010.

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author, Bell Genevieve, Gregg Melissa 1978 author, and Seaver Nick 1985 author, eds. Data, now bigger and better! Chicago: Prickly Paradigm Press, 2015.

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RuleML 2009 (2009 Las Vegas, Nev.). Rule interchange and applications: International symposium, RuleML 2009, Las Vegas, Nevada, USA, November 5-7, 2009 : proceedings. Berlin: Springer, 2009.

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Duke, D. J. (David J.), ed. Semantic multimedia: Third International Conference on Semantic and Digital Media Technologies, SAMT 2008, Koblenz, Germany, December 3-5, 2008 : proceedings. Berlin: Springer, 2008.

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Maybury, Mark T. Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring. IEEE Computer Society Press, 2012.

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Maybury, Mark T. Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring. IEEE Computer Society Press, 2012.

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Частини книг з теми "Metadata mining"

Hacke, Melanie. "Chapter 4. Metadata mining." In Literary Translation in Periodicals, 95–120. Amsterdam: John Benjamins Publishing Company, 2020. http://dx.doi.org/10.1075/btl.155.04hac.

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Rousidis, Dimitrios, Paraskevas Koukaras, and Christos Tjortjis. "Examination of NoSQL Transition and Data Mining Capabilities." In Metadata and Semantic Research, 110–15. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71903-6_11.

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Segura, Alejandra, Christian Vidal, Victor Menendez, Alfredo Zapata, and Manuel Prieto. "Exploring Characterizations of Learning Object Repositories Using Data Mining Techniques." In Metadata and Semantic Research, 215–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04590-5_20.

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Seol, Jae-Wook, Won-Jun Choi, Hee-Seok Jeong, Hye-Kyong Hwang, and Hwa-Mook Yoon. "Reference Metadata Extraction from Korean Research Papers." In Mining Intelligence and Knowledge Exploration, 42–52. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05918-7_5.

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Jiang, Tao, and Ah-Hwee Tan. "Mining RDF Metadata for Generalized Association Rules." In Lecture Notes in Computer Science, 223–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11827405_22.

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Zhao, Xiaoyong, Yang Yang, Li-li Sun, and Han Huang. "Metadata-Aware Small Files Storage Architecture on Hadoop." In Web Information Systems and Mining, 136–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33469-6_20.

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Merrett, T. H. "Attribute Metadata for Relational OLAP and Data Mining." In Database Programming Languages, 97–118. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46093-4_6.

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Tejasree, S., and Shaik Naseera. "Improved Clustering Technique Using Metadata for Text Mining." In Innovations in Computer Science and Engineering, 243–50. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7082-3_29.

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Tan, Rong, Junzhong Gu, Zhou Zhong, and Peng Chen. "Metadata Management of Context Resources in Context-Aware Middleware System." In Web Information Systems and Mining, 350–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33469-6_46.

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Cui, Binge, and Jie Zhang. "An Intelligent Metadata Extraction Approach Based on Programming by Demonstration." In Web Information Systems and Mining, 678–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33469-6_84.

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Тези доповідей конференцій з теми "Metadata mining"

Arotaritei, Dragos. "Data mining in metadata repositories." In AeroSense 2002, edited by Belur V. Dasarathy. SPIE, 2002. http://dx.doi.org/10.1117/12.460213.

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"Computational Linguistics for Metadata Building (CLiMB) Text Mining for the Automatic Extraction of Subject Terms for Image Metadata." In International Workshop on Metadata Mining for Image Understanding. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0002338100030012.

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"Combining Visual and Text Features for Learning in Multimedia Direct Marketing Domain." In International Workshop on Metadata Mining for Image Understanding. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0002337200340047.

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"Automatic Image Annotation using Visual Content and Folksonomies." In International Workshop on Metadata Mining for Image Understanding. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0002337600580066.

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"Travel Blog Assistant System (TBAS) - An Example Scenario of How to Enrich Text with Images and Images with Text using Online Multimedia Repositories." In International Workshop on Metadata Mining for Image Understanding. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0002338200900104.

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"Describing the Where – Improving Image Annotation and Search through Geography." In International Workshop on Metadata Mining for Image Understanding. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0002338401050114.

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"Which Strategy to Combine Face Identification Tools with Clothing Similarity: Contesting or Reinforcing?" In International Workshop on Metadata Mining for Image Understanding. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0002338500780089.

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"Improved Image Retrieval using Visual Sorting and Semi-Automatic Semantic Categorization of Images." In International Workshop on Metadata Mining for Image Understanding. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0002339400670077.

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"Can Feature Information Interaction Help for Information Fusion in Multimedia Problems?" In International Workshop on Metadata Mining for Image Understanding. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0002339500230033.

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"Extracting Semantic Meaning from Photographic Annotations using a Hybrid Approach." In International Workshop on Metadata Mining for Image Understanding. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0002339700480057.

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