Metadata Based Approach for Web Multimedia Model

Lecture Notes in Computer Science, 2009

In this paper, we focus on the managing of multimedia document and more precisely on the annotation and the generation of adaptable multimedia documents. Our solution is directed towards analysing the ways to "bridge the gap" between physical and semantic levels, for multimedia document modelling and querying. Our goal is to describe how to model and unify features elicited from content and structure mining. These descriptors are built from the various features elicited from the multimedia documents using available processing techniques. The personalization enables dynamic re-structuring and reconstruction of hypermedia documents answering to the user queries. However, more factors should be considered in handling hypermedia documents. Once queried, documents can be adapted by using an indexing scheme, which exploits multiple structures. We can process queries efficiently with minimal storage overhead. We suggest for that, the adaptation of multimedia document content with user needs and preferences. This approach is based on the OOHDM methodology extension with the use of the metadata.

Web Multimedia data mining (WMDM) can be defined as the process of finding interesting patterns from media data such as audio, video, image and text that are not ordinarily accessible by basic queries and associated results. MDM is the mining of knowledge and high level multimedia information from large multimedia database system. MDM refers to pattern discovery, rule extraction and knowledge acquisition from multimedia database. To extract knowledge from multimedia database multimedia techniques are used. We compare MDM techniques with the state of the art data mining techniques involving clustering, classification, sequence pattern mining, association rule mining and visualization. This paper is a review on Web multimedia mining (WMM) and Knowledge discovery it elaborates basic concepts, application at various areas, techniques, approaches and other useful areas which need to be work for WMM. Analyzing this huge amount of multimedia data to discover useful knowledge is a challenging problem which has opened the opportunity for research in WMM and knowledge discovery.

IEEE Multimedia, 2000

Due to the progressively increasing amount of multimedia on the Web, the need for efficient metadata formats describing that content has become increasingly evident. This paper gives an overview of the different approaches and methods for creation and retrieval of semantic rich multimedia metadata. Semantic web and its most important technologies XML, RDF and ontologies used for multimedia annotation are defined. An overview of various multimedia metadata vocabularies and formats that vary in their size and purpose is provided. Multimedia metadata is a type of metadata used for describing different aspects of multimedia content. All formats of multimedia metadata are not compatible with each other and most of it do not provide enough semantics. New Semantic Web technologies provide well-defined information meaning so different multimedia metadata can be more easily processed by computers.

The astonishing growth of videos on the Internet such as YouTube, Yahoo Screen, Face Book etc, organizing videos into categories is of paramount importance for improving user experience and website utilization. In this information age, video information is the rapidly sharing by the people through social media websites such as YouTube, Face Book, yahoo Screen etc. Different categories of web video are shared on social websites and used by the billions of users all over the world. The classification/partitioning of web videos in terms of length of the video, ratings, age of the video, number of comments etc, and analysis of this web video as a unstructured complex data is a challenging task. In this work we propose effective classification model to classify each category of web-videos (Ex- ‘Entertainment’, ‘People and Blogs’, ‘Sports’, ‘News and Politics’, ‘Science and Technology’ etc) based on other web metadata attributes as splitting criteria. An attempt is made to extract metadata from web videos. Based on the extracted metadata, web videos are classified/partitioned into different categories by applying data mining classification algorithms such as and Random Tree and J48 classification model. The classification results are compared and analyzed using cost/benefit analysis. Also the results demonstrate classification of web videos depends largely on available metadata and accuracy of the classification model. Classification/partitioning of web-based videos are important task with many applications in video search and information retrieval process. However, collecting metadata required for classification model may be prohibitively expensive. The experimental difficulties arise from large data diversity within a category is pitiable of metadata and dreadful conditions of web video metadata.

—Image Annotation is a method to reveal the meaning for a specific image .The embedded meaning in the image is identified and mined. The Scenario is identified through the image annotation scheme with in a provided training. The focus is on the blur images, noisy images and images with pixels lost. The image annotation can be done on the good resolution image. The analysis carried outon the image data to derive the information and image restoration takes place. Image mining deals with extracting embedded details, patterns and their relationship in images. Embedded details in the image could be extracted using high-level features that are robust. Inpainting techniques can be utilized for cleaning the image .The analytics is applied on enormous amount of data, techniques performed on the test images sets for better accuracy.

ABSTRACT This paper proposes a new multimedia ontology based scheme for semantic multimedia data processing on the web. The ontology language” Multimedia Web Ontology Language”(MOWL), is designed as an extension of OWL, the W3C recommended ontology language for the web. MOWL supports creation of and reasoning with perceptual modeling of concepts, and probabilistic evidential reasoning. Index Terms—Multimedia systems, Ontology, Semantic

During the last few years, there was a large increase in various forms of multimedia content on the Web, which presents a growing problem for the further use and retrieval of such content. In parallel, with the increase of multimedia content on the Web existing multimedia metadata standards were improved and new standards have been developed. To facilitate the use of multimedia content on the Web, that content is assigned a metadata that describes it. Manually annotation is time-consuming and expensive process. Besides, annotations can be created by different people such as authors, editors, publishers or the end users, which represents a problem, because there may be different interpretations of those annotations. The main disadvantage of such annotations is the lack of well-defined syntax and semantics which is why computers in most cases can hardly process such information. Using Semantic Web technologies such as XML, RDF and ontologies is recommended for creating new and enrichi...

The recent era is of sharing multimedia data globally. Every second thousands of images get uploaded over social network. It’s becoming very challenging issue to search proper multimedia data over the internet. Big Data is an immense dataset, connected to usually utilized software tools, whose size is huge, which is past the capacity of hardware regarding catch, oversee, and handle the information inside a satisfactory passed time. As multimedia is increasing rapidly so that to manage this large amount of multimedia data there is urgent requirement of the system to handle and process this data in an efficient way. Proposed framework utilizing a search engine, which joins both text and information based forms to enhance picture recovery execution. In the search, there is a superior probability to admission description and textual components firstly, text background are organized. In proposed system web image tagging as well as image description are considered to re-arrange the multimedia data. Here system is implemented in which number of user can request for the data and admin has privilege to add the data to the dataset. Then finally images gets rearranged according to the semantic signatures. To get the result, number of images are taken as input and processed on it. The outcome is produced by contrasting the client query, information present in server of pictures and the tags given by administrator while transferring the picture. Last result demonstrates that this framework is having better execution and gives exact result.

2007 International Workshop on Content-Based Multimedia Indexing, 2007

The management and exchange of multimedia data is challenging due to the variety of formats, standards and intended applications. In addition, production of multimedia data is rapidly increasing due to the availability of off-the-shelf, modern digital devices that can be used by even inexperienced users. It is likely that this volume of information will only increase in the future. A key goal of the MUSCLE (Multimedia Understanding through Semantics, Computation and Learning) network is to develop tools, technologies and standards to facilitate the interoperability of multimedia content and support the exchange of such data. One approach for achieving this was the creation of a specific "E-Team", composed of the authors, to discuss core questions and practical issues based on the participant's individual work. In this paper, we present the relevant points of view with regards to sharing experiences and to extracting and integrating multimedia data and metadata from different modes (text, images, video).

Multimedia Tools and Applications, 2008

In this paper we present a framework for unified, personalized access to heterogeneous multimedia content in distributed repositories. Focusing on semantic analysis of multimedia documents, metadata, user queries and user profiles, it contributes to the bridging of the gap between the semantic nature of user queries and raw multimedia documents. The proposed approach utilizes as input visual content analysis results, as well as analyzes and exploits associated textual annotation, in order to extract the underlying semantics, construct a semantic index and classify documents to topics, based on a unified knowledge and semantics representation model. It may then accept user queries, and, carrying out semantic interpretation and expansion, retrieve documents from the index and rank them according to user preferences, similarly to text retrieval. All processes are based on a novel semantic processing methodology, employing fuzzy algebra and principles of taxonomic knowledge representation. The first part of this work presented in this paper deals with data and knowledge models, manipulation of multimedia content annotations and semantic indexing, while the second part will continue on the use of the extracted semantic information for personalized retrieval.

1998

This book brings together the foremost experts from around the world to give you the latest developments in managing digital media in information systems. This book offers real guidance on creating and using metadata--data that's extracted from data to be stored--for use in the complex job of managing multimedia. Packed with new insights, new technologies, and information on practices and standards, Multimedia Data Management brings you up to speed on metadata definitions, types, and tactics of all variety of digital ...

Multimedia Tools and Applications, 2012

Abstract: Question answering is the most common technique to collect information from different sources. One such source which gave rise in recent internet technology is Community Question Answering (cQA). Earlier the cQA gave information only in textual format which is not very much informative for many questions posted by the user, however it is natural to extend text based question answering research to Multimedia Question Answering which gave importance for many reasons. First most video contents are indexed with metadata, second many questions are better explained with the help of non textual medium, third media contents especially videos are now used to convey many types of information. In this project a technique is proposed which enriches textual answers with appropriate media data. This technique consists of three components to enrich textual data: Answer medium selection, Query generation for multimedia search, Multimedia data selection and Presentation. The multimedia question answering complements text QA with whole QA paradigm i.e., image video along with text. This approach or technique automatically selects media information for appropriate textual answer questioned by users or community members. Here Question answering languages leverages advance media content, linguistic analysis, and domain knowledge to return precise answers to questions posted by community members. Keywords: Question Answering, Answer Medium Selection Title: Generating Multimedia Information Using Web Data mining Author: SharathBabu.S, Ravi.M International Journal of Computer Science and Information Technology Research ISSN 2348-120X (online), ISSN 2348-1196 (print) Research Publish Journals

2005

Abstract The Semantic Web is about adding formal structure and explicit semantics to web content for the purpose of more efficient information management and access by both humans and computers. Through the use of machine understandable semantics, web resources become much easier and more readily accessible. Populating the Semantic Web with multimedia metadata requires for appropriate technologies to support both the analysis (metadata extraction) and the annotation (metadata creation) processes.