Guest Editorial
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| Title | Guest Editorial |
| Authors | |
| Abstract | Today, the popularity of multimedia demands efficient and intelligent strategies to cope with large amount of multimedia data and the real time constraints of applications. Recent efforts in the area of Multimedia Retrieval Systems (MMRS) have led to a growing research community and a number of projects on the international, national and industrial level.Besides concentrating on single media retrieval systems (e.g., in which only images are considered), the latest technologies target on multimodal and/or semantically rich retrieval engines. This development explicitly forms the mainstream trend as queries such as “Show me the movie and related material for the given score available by melody and text snippets” (maybe by humming) or “Give me all media (text, image, video, audio) containing information about the city of Paris” come into vogue. In order to support those challenging requests, research needs to work on a) new (ontology-based) semantic models for combining individual media models, b) new retrieval engines able to cross the media boundary during search and c) new interfaces that can deal with various media data inputs and present complex multimedia information. For instance, similarity metrics need to be developed/modified encompassing the media boundary, with the aim to discover useful relationships among multimodal multimedia documents and to find a better way through-out the vast amount of media information.For this purpose, theories and techniques concerning multimodal information retrieval systems focusing on new approaches for indexing, representing, organizing, integrating, clustering, querying and feature extraction of multimodal data need to be investigated and evaluated.The content of the special issue aims on providing a deeper look of current research in the area of Multimodal Multimedia Retrieval including both theory and application oriented papers and new approaches in extraction and use of semantic concepts in order to minimize the semantic gap in multimedia retrieval.Using the MPEG Query Format for Cross-Modal Identification Matthias Gruhne, Peter Dunker, Ruben Tous This article demonstrates the new multimedia query language (MPEG Query Format) in a distributed cross modal retrieval environment.Bridging the Semantic Gap for Texture-based Image Retrieval and Navigation Najlae Idrissi, Jos´e Martinez, Driss Aboutajdine The authors in this paper propose a new approach for interpreting textures in natural terms in order to bridge the semantic gap in image retrieval.Semantic Restructuring of Natural Language Image Captions to Enhance Image Retrieval Kraisak Kesorn, Stefan Poslad Image captions provide useful information and hints for image retrieval. The article introduces a framework that combines Natural Language Processing approaches with Ontologies and LSI in order to extract concepts in image captions.Semantic Concept Mining Based on Hierarchical Event Detection for Soccer Video Indexing Maheshkumar H. Kolekar, Kannappan Palaniappan, Somnath Sengupta, Gunasekaran Seetharaman The detection of semantic concepts in the sports video domain is a challenging task. This article introduces a novel hierarchical framework that supports event sequence detection, semantic concept allocation (e.g., goal scored) and summarization.A Multimodal Data Mining Framework for Revealing Common Sources of Spam Images Chengcui Zhang, Wei-Bang Chen, Xin Chen, Richa Tiwari, Lin Yang, Gary Warner Spamming is an overwhelming problem in the today’s communication flow. Related to this, the proposed framework provides means for detecting and clustering spam images in order to track spam gangs.Multimodal Preference Aggregation for Multimedia Information Retrieval Eric Bruno, Stephane Marchand-Maillet The authors present a novel information representation for multimodal data in combination with a machine-learning based retrieval algorithm and highlight their improved efficiency in contrary to the SVM algorithm.The editors want to thank all reviewers for their excellent work during the review process:Beek, Peter van; Sharp Laps, USA Boll, Susanne; University of Oldenburg, Germany Böszörmenyi, Laszlo; Klagenfurt University, Austria Carreras, Anna; DMAG-UPC/UPF, Spain Choi, Miran; ETRI, Korea Cordara, Giovanni; Telecom Italia Lab, Italy Gandhi, Bhavan; Motorola Labs, USA Granitzer, Michael; Know-Center ,Austria Gruhne, Matthias; Fraunhofer (IDMT), Germany Linaza, María Teresa; VICOMTech, Spain Mass, Yosi; IBM, Isreal Melby, Alan K.; Brigham Young University, USA Oria, Vincent; NJIT, USA Pereira, Fernando; IST, Portugal Sang Kyun Kim; Samsung, South Korea SooJun Park; ETRI, South Korea Tous, Ruben; DMAG-UPC/UPF, Spain Tsinaraki, Chrisa; Technical University of Crete, Greek Vetro, Anthony; Mitsubishi Electric Research Laboratories, USA Wolf, Ingo; T-Systems, Germany Yoon, Kyoungro; Konkuk University, Korea Zaharieva, Maia; TU Wien, Austria Zhao, Jun; University of Oxford, UK |
| Publisher | ACADEMY PUBLISHER |
| Date | 2009-10-01 |
| Source | Journal of Multimedia Vol 4, No 5 (2009): Special Issue: Multimodal Multimedia Retrieval |
| Rights | Copyright © ACADEMY PUBLISHER - All Rights Reserved.To request permission, please check out URL: http://www.academypublisher.com/copyrightpermission.html. |