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Video Data Modelling Based on the MPEG-7 Standard in e-Learning. Cloud Computing. Andrei Marius Gabor *, Politehnica" University of Timisoara, Department ...
AWERProcedia Information Technology & Computer Science Vol 04 (2013) 174-185

3rd World Conference on Innovation and Computer Sciences 2013

Video Data Modelling Based on the MPEG-7 Standard in e-Learning Cloud Computing Andrei Marius Gabor *, Politehnica" University of Timisoara, Department of Communications, Romania. Radu Vasiu, Politehnica" University of Timisoara, Department of Communications, Romania.

Suggested Citation: Gabor M., A. & Vasiu, R. Video data modelling based on the MPEG-7 standard in e-learning cloud computing. AWERProcedia Information Technology & Computer Science. [Online]. 2013, 04, pp 174-185. Available from: www.awer-center.org/pitcs Received December 08, 2012; revised February 16, 2013; accepted March 04, 2013. Selection and peer review under responsibility of Prof. Dr. Fahrettin Sadıkoglu, Near East University. ©2013 Academic World Education & Research Center. All rights reserved. Abstract In recent years the phenomenal development of content and multimedia services, as well as the technical progress of mobile devices (smartphone, tablets), have led to a new direction in the educational field, based on Cloud Computing. Services provided and applications offered through Cloud Computing at a relatively low cost, together with running them on a scalable infrastructure, will determine future migration towards this kind of platforms. The advantage offered by diversity, flexibility and virtualization of multimedia resources that can all be made available to the user in Cloud Computing, requires their effective management, for use or reuse in courses. Thus, the use of some domain-specific ontologies for e-learning, for annotation of the LO (Learning Object) content and LOM (Learning Object Metadata) structure, will result in browsing, search and optimal retrieval. This paper proposes a system’s architecture for video data modelling through annotation, semantic classification and content retrieval based on the MPEG-7 standard that makes use of the Cloud Computing benefits. Keywords: Cloud Computing, ontologies, semantic web, MPEG-7, content retrieval, e-learning;

* ADDRESS FOR CORRESPONDANCE: Andrei Marius Gabor, Politehnica" University of Timisoara, Department of Communications, Romania Andrei Marius Gabor, E-mail Address: [email protected] / Tel.: +4-074-504-4469

Gabor M., A. & Vasiu, R. Video data modelling based on the MPEG-7 standard in e-learning cloud computing. AWERProcedia Information Technology & Computer Science. [Online]. 2013, 04, pp 174-185. Available from: www.awer-center.org/pitcs

1. Introduction The need to develop information in education field is constantly increasing and with it, the need to improve e-learning solution, through developing applications for this field. Applications should run over a wide range of devices from PCs to smart-phones and tablets, in order to keep up with technology to a new paradigm called Cloud Computing. Due to its resources, Cloud Computing has become efficient to the forms of electronic learning, because it offers the opportunity to teach and learn in a virtual environment as well as to develop the scalable web application. The Cloud Computing offers the solution to access applications without requiring software with license, updates, database infrastructure, all these being offered by the provider at a low price. Combining Cloud Computing, communications infrastructure, computing devices, services and web applications, users can access online through the browser, a huge storage space with unlimited computing power. Thus, Cloud Computing offers a variety of services in a virtual environment and allows the users to use web interfaces to launch applications with a variety of operating systems. Due to these advantages Cloud Computing has proved to be attractive to institutions and universities in order to keep up with rapid developments in IT, but also in terms of prices and reduced staffing costs and services. The video content has an important role in education process being used for consumption or recreation during the courses. Thus it is important to develop a mechanism to describe and organize the video content, to allow users the quick access to information. MPEG-7 provides this advantage of describing the content, and then it helps to search, browse and retrieve it efficiently for the benefit of users of e-learning platforms. Current standards require the need to reuse the entire content or parts of it, called LO (Learning Object). According to IEEE (Learning Technology Standards Committee, IEEE P1484.12.1-2002), the learning object is defined as any digital or non-digital entity that can be used or reused in e-learning technology. More e-learning objects are assembled to form the courses and, they are released to consumption, to users. The proposed paper describes the architecture of a system that manages the video data in elearning platforms, hosted in the "Cloud", through semantic annotation based on the ontology of the content and domains structure. It also describes the efficient retrieval in order to be used or reused during the courses. Query content and its reuse, makes use of MP7QF framework, specific MPEG-7 standards, followed by the exploitation of the similarity clips and grouping them according to the hierarchical cluster. For the beginning we have described models of services and essential characteristics in Cloud Computing. After that, we used semantics for annotation of the video content, retrieval and clustering results based on video relevance as a result of query. Finally, we described the architecture of a system which models semantically the video content using Polysema application, and we described the benefits of Cloud Computing in educational environment. 2. Services Models and Features in Cloud Computing In recent years, the nature of Internet has changed progressively, thus, leading to applications that belong to the smart web, applications that run over a wide range of mobile devices, from smartphones, PDA to tablets. Mobile devices have limitations related to the storage space of the applications, multimedia data so, the use and storage in the "Cloud" is the most evident characteristic of Cloud Computing. Thus, Cloud Computing can be considered a model of computation based on the Internet, where the main task is to ensure the access to the user and to provide the user with the easiest way of handling the available resources. A giant potential of technology is next to us in order to reduce the expenses made by institutions and universities which are related to infrastructure 175

Gabor M., A. & Vasiu, R. Video data modelling based on the MPEG-7 standard in e-learning cloud computing. AWERProcedia Information Technology & Computer Science. [Online]. 2013, 04, pp 174-185. Available from: www.awer-center.org/pitcs

hardware and software. The software resources, hardware and infrastructure resources in Cloud Computing are considered services and are hosted in the “Cloud” [1]. Cloud Computing moves the processing effort done by a mobile device to cloud data center that belongs to it; in this respect the device can process the data using the resources and storage space of the cloud data center. The providers of Cloud Computing offer four model services, and five essential characteristics of Cloud Computing, as described in Figure 1, as follows: IaaS, PaaS, SaaS and in December 2012 was introduced by ITU (ITU-T Newslog-Cloud Computing and Standardization: Technical Reports Published “International Telecommunication Union Retrieved, 16 December 2012”), as part of Cloud Computing and Network as a Service (NaaS). The essential characteristics of Cloud Computing are: On demand self service, which helps a user to receive automatically the computing capabilities. Broad network access, through which the network resources are accessed through a standard mechanism, which promotes the mobile devices.

Figure 1. Model of Cloud Computing

Rapid elasticity, where the resources are provided quickly, automatically and unlimited. Measure service a feature that automatically controls the resources, through mobilization of capabilities, related to the type of service used, and the last characteristic is Resource pooling, which re-unites the resources in order to serve more users and to store and processing data. 3. Video Data Modelling in E-Learning Cloud Computing With the advent of powerful computers and increasing the communications bandwidth, the distance education began to meet new directions of development. Due to the large volume of information transmitted, the video data are increasingly used in courses. The main challenge in re176

Gabor M., A. & Vasiu, R. Video data modelling based on the MPEG-7 standard in e-learning cloud computing. AWERProcedia Information Technology & Computer Science. [Online]. 2013, 04, pp 174-185. Available from: www.awer-center.org/pitcs

using the content efficiently is given by the indexing of the video information and by the manual or automatic annotation, so as to form a more precise description of the content. The richer the content is, from semantic point of view, the more difficult it is for the annotation task to be fulfilled. The standard for video content description is MPEG-7 [2], which allows the description of scenes, objects, referring to the information about their creation (author, title, data, location), media information (size, quality, coding), textual and semantic annotation [3]. 3.1. Annotation Based on Ontology In the e-Learning platforms the accuracy for searching and identification of the content has increased using the ontology. Such an ontology for annotating the content of learning object, that allows analysis, recognizing concepts and the relation between them was proposed and developed in a previous paper [4]. The LOM (Learning Object Metadata) ontology created based on WordNet was integrated into the application Polysema and is described in Figure 2. The video grouping based on clusters (collections of similar clips) and sub-clusters (semantically similar), determines a better retrieval of the relevant videos.

Video Course Course type course tutorial conference Language Author Time Creation date Difficulty

Video Lesson

Time has Content Start Time Finish Time

Video Segment

Time has Segmented Start Time

Finish Time

Learning Object

Figure 2. LOM Ontology Description

Modelling the video content in e-Learning platforms, based on textual annotation, involves a semantic model intended to replace the video stream and involves the following aspects [5]: 

Splitting of the video stream into events, which means shorter segments of the video streaming described by attributes and proprieties of learning object;



The existence of some temporal relations between events that allows the semantic model to express the dynamism of learning object;



The existence of spatial relations between learning objects.

3.2. The MP7QF framework for retrieval content The content retrieval based on semantic concepts describes the possibility of a video clip to be linked to the concept and classified as a relevant or irrelevant clip. Thus, the task of the retrieval of the video content is to search in a large video collection in order to retrieve the information expressed by the user through query. MP7QF defines the format for the query content being responsible for the search and retrieval of it. It must also allow simultaneous searching in multimedia database, which are 177

Gabor M., A. & Vasiu, R. Video data modelling based on the MPEG-7 standard in e-learning cloud computing. AWERProcedia Information Technology & Computer Science. [Online]. 2013, 04, pp 174-185. Available from: www.awer-center.org/pitcs

located in implementation model of the Cloud, Private, Public or Hybrid, described in Figure 3. An important feature is that during the retrieval process of data, the MP7QF must accept the video formats, returned by the query made by the user.

M P7

Learning Management System

QF

Private

MP7QF Public

M P7

QF

Hybrid

Figure 3. MP7QF Query

The MP7QF is based on XML in the sense that all MP7QF instances (query and answers), require XML documents. The MP7QF format is standardized ISO/IEC 15938-12 (ISO/IEC, 2004). The SCORM reflects the tendency to unify metadata specification as a specialized subset that describes a RLO (Reusable Learning Object), based on content, by RIO (Reusable Information Object). The RIO strategy is to form small pieces of media information to be reused in the context and the content of the courses. The MP7QF query is a document, which validates MP7QF Schema, and always includes mp7qf: Query and mp7qf:Input, being described as follows: < m7qf:QueryCondition > < m7qf:QueryExpression > < m7qf:SingleSearch xsi:type =" < m7qf:TextQueryType "retrieval =" contains "> < m7qf:FreeTextQuery > Figure 4. MP7QF Query Based on Textual Description

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Gabor M., A. & Vasiu, R. Video data modelling based on the MPEG-7 standard in e-learning cloud computing. AWERProcedia Information Technology & Computer Science. [Online]. 2013, 04, pp 174-185. Available from: www.awer-center.org/pitcs

3.3. Semantic Similarity of Video Data It is possible that as a result of the textual query, referring to a particular topic or concept, the clips, which show semantic similarities with the query subject, be relevant to the initial query. However, these clips may include individual concepts without containing relations between them. Thus a reordering of them is required in order to make them relevant to the query, described in Figure 5. A general framework proposed by Gabor and Gaga [5], calculates the semantic similarity for relevant clips, followed by the calculation of semantic similarities between concepts and grouping the results in the form of hierarchical clusters based on distance between cluster objects described in the relations shown below:

F  x, ci , y   a  p  ci | y   1  a   p  x | yi 

(1)

where a   0,1 p  ci | y  is the posterior probability (Bayes), the probability to find a c i clip similar with y , great relevance in the node p  x | yi  is the likelihood function that clip x found, to be in group consisting of clips y i .

Text Text Text Clip 1, 2, 3,...,n

MP7QF (text, subject)

Cluster analysis

Clusters

Cluster similarity

Video clip

Video Re-ranking

Figure 5. The Video Retrieval

Based on WordNet, researchers have presented different formulas to calculate semantic similarity. The following formula was described in Lescock and Chodorow paper [6], to calculate the semantic similarity between two concepts in relevant clips:

sim( 1 ,  2 )   log

len ( 1 ,  2 ) 2 max  wordnet depth ( )

(2) 179

Gabor M., A. & Vasiu, R. Video data modelling based on the MPEG-7 standard in e-learning cloud computing. AWERProcedia Information Technology & Computer Science. [Online]. 2013, 04, pp 174-185. Available from: www.awer-center.org/pitcs

where:

len(1 , 2 )

is the length of the shortest path between two concepts  1 and  2 of WordNet

depth( )

the path length from  to root

Based on the expression (2) and the work of Wang et al [7] we propose the following formula to calculate semantic similarity:

sim( x , ci ) 

log(miny  ci )

 dist( y , ci )   dist(x, yi )

(3)

Grouping the video clips that are, based on semantic similarity in the form of hierarchical cluster is obtained by calculating the distance between standardized vectors and making an array of the distances that have the element:

Ai , j  d (ci , c j )

(4)

After this cluster grouping, we check if the hierarchical cluster indicates similar objects groups using the formula: n

m

 d (ci , c j ) d (C1 , C 2 ) 

i 1 j 1

m*n

(5)

where:

C1 , C2 ci , c j

clusters cluster objects The importance of the cluster will be given by:

v(n) 

sim(i)

 ni

(6)

where: sim(i) clip's similarity

180

Gabor M., A. & Vasiu, R. Video data modelling based on the MPEG-7 standard in e-learning cloud computing. AWERProcedia Information Technology & Computer Science. [Online]. 2013, 04, pp 174-185. Available from: www.awer-center.org/pitcs

 ni

standard deviation

Semantic cluster described above is designed to improve the performances of search and retrieval of video data in e-Learning systems allowing a better management of the course content. 4. Related Work There are several researches and projects related to Cloud Computing with applicability in education field, which describe the advantages and potential of this new paradigm. Thus, in 2007 IBM and Google announced the concept of Cloud Computing in the universities in order to improve student’s knowledge. Casquero et al [8] describes a framework that integrates the institutional services in order to support the daily activities based on Google Gadgets and Google Apps. When comparing multimedia applications running on a desktop to those on a mobile, we can say that the latter are seriously limited by the processing power, which determines the use and processing of multimedia data on server side. Pocatilu et al [9], Boja et al [10], to analyze the e-learning applications on the mobile side and how to optimize them. Praveena and Betsy [11] have described the application of Cloud Computing in universities and Ouf et al [12] described and presented an eLearning system based on Cloud Computing and WEB 2.0. Masoud and Huang [13], show the features of e-Learning evaluating the e-Learning concept in educational field and describes the architecture of such a system. Madan et al in [14] also describe the architecture of an e-Learning system and show the benefits of screen casting tools through which the user can create and manage videos through the browser. Wu et al [15] propose an architecture that enables online and offline applications function based on standards, which can be integrated with other platforms. Thus other types of applications can be adapted or built without changing the architecture. However, the great developers in Cloud Computing are well-known companies such as Google, Amazon, Yahoo, Adobe, IBM, Microsoft and many others.

5. E-Learning Architecture Proposed The aim of the architecture is to provide a semantic description of video content necessary for consumption or construction of new courses. Each video clip is described using a MPEG-7 document, based on a corresponding ontology. E-learning system architecture that manages the video data semantically contains: the client application (web browser), which runs on a mobile device (smartphone, tablet) or PC, the server application, database and hardware components (servers, infrastructure).

181

Software

Gabor M., A. & Vasiu, R. Video data modelling based on the MPEG-7 standard in e-learning cloud computing. AWERProcedia Information Technology & Computer Science. [Online]. 2013, 04, pp 174-185. Available from: www.awer-center.org/pitcs

Service Management Component

Retrieval Component

Ontology Builder

App Component

Platform

Mobile App

Elasticity Manager

Broad Network Access

Polysema - semantic annotation

Infrastructure

On Demand Self Service

Domain Ontology

MPEG-7 Database

User Management

Security Management

Figure 6. The Architecture Proposed

The proposed architecture integrates the Polysema application in Cloud Computing. It annotates semantically the video content based on the developed ontology, on the structure of the content of the domain. The infrastructure level contains information about users’ management, security, learning resources.

Figure 7. Displaying the Polysema Application on Mobile Device

The platform level contains the video and MPEG-7 document database and resources for the application developers. The software level and the applications contain resource and specific capabilities for integrating of the courses, the educational resource and way of retrieving and reuse the query MPEG-7 results. The user can define his preference through Service Management Component, regarding the use of a particular service, ontology or can establish some rules for 182

Gabor M., A. & Vasiu, R. Video data modelling based on the MPEG-7 standard in e-learning cloud computing. AWERProcedia Information Technology & Computer Science. [Online]. 2013, 04, pp 174-185. Available from: www.awer-center.org/pitcs

semantic annotation of certain video clips. In Ontology Builder one can build and then use ontologies of the domain and content structure using Protégé application. An important aspect that was taken into account when talking about the developing of the architecture, using the Polysema application, was that it is compatible with current technologies of semantic annotation of video data, and use the specific ontologies in e-learning field. Thus, MPEG-7 describes the content, for each video scene (produced by video indexing) by providing specific descriptions, which present an idea or important topic. After the completion of the annotation process, an MPEG-7 file will be generated, as described in Figure 8, to be used further in the video retrieval process.

Figure 8. MPEG-7 File 6. The Benefits of e-Learning in Cloud Computing

In e-Learning system hosted in the “Cloud” a great emphasis is placed on data security and on applying some data backup solutions for data center. The main advantage of Cloud Computing is that those devices with low performances concerning memory and processing can access the e-Learning platforms using their resources for storing and processing multimedia data. Thus, Cloud has the ability to store and process any amount of multimedia data anytime, anywhere. This way, developers are given access to the scalability, reliability, resources and applications in the cloud. Users don’t have to invest large sums to create the infrastructure, server data and applications, which are offered by the cloud providers. Another important aspect that Cloud offers is the virtualization that allows the virtual creation of some system without major costs. It is relatively easy to create a clone or a virtual machine. The Cloud Computing improves the unlikeliness because it is very difficult (if not impossible) to determine where the datacenter is located; you can only approximate the area or the geographical area. The use of domain-specific ontologies for the entire 183

Gabor M., A. & Vasiu, R. Video data modelling based on the MPEG-7 standard in e-learning cloud computing. AWERProcedia Information Technology & Computer Science. [Online]. 2013, 04, pp 174-185. Available from: www.awer-center.org/pitcs

e-Learning platform aims a better annotation and retrieval of the content made by user through a query.

7. Conclusions and Future Research

This paper presents the architecture of an e-Learning system based on cloud Computing, in order to build a virtual learning environment which combines a wide range of technologies (described and presented in our previous papers) to annotate and retrieve video content with the main objective of handling efficiently the management courses and the education material. This way, Cloud Computing can help the institutional process through a new paradigm. Such a large amount of data can be made available to students and teachers to be accessed. From theoretical perspective, our contribution to this paper was accomplished by presenting an architecture that includes various technologies for video content annotation and retrieval of it in eLearning platforms. In practice, these technologies are to be implemented in Polysema application. The future research will focus on the development of new optimized algorithms for retrieval of the video data by exploiting and combining the MPEG-7 high level descriptors with the low level ones in order to achieve greater efficiency in the process of finding and making use of the benefits, offered by Cloud Computing Acknowledgements

“This work was been partially supported by the strategic grant POSDRU 107/1.5/S/77265, inside POSDRU Romania 2007-2013 co-financed by the European Social Fund – Investing in People.” References Zhang, L. J., Zhou, Q. “CCOA: Cloud Computing Open Architecture”. In Proceedings of the 2009 IEEE International Conference on Web Services, 2009, pp. 607-616. ISO/IEC 15938 version 2 (2004) Information technology—Multimedia content description interface (MPEG-7). Salembier, P.:”MPEG-7 Multimedia description schemes”. IEEE TA Circuits Systems Video Technol. Vol. 11(6), 2001, pp. 748–759. Gabor A.M., Vasiu R., "Interdisciplinarity in e-Learning Platforms Based on Textual Annotation", Information and Software Technologies Communications in Computer and Information Science, Volume 319, Springer 2012, pp 362-372. Gabor, A.M., Gaga L., "Retrieval the Video Data in e-Learning Platforms". In Proceedings of the International Symposium Research and Education in Innovation Era, 2012, pp 43-50. C. Leacock and M. Chodorow. Combining local context and wordnet similarity for word sense identification. In WordNet: An Electronic Lexical Database, 1998, pages 265–283. Wang R., Jiang S., Zhang Y., Wang M., “Re-ranking search result using semantic similarity”, Proc. IEEE Fuzzy Systems and Knowledge Discovery, 2011, pp. 1047-1051. Casquero O., Portillo J., Ramón Ovelar R. , Romo J., and Benito M., ”iGoogle and gadgets as a platform for integrating institutional and external services,” 2008, MUPPLE’08, pp. 37-41. Pocatilu P., Boja C.,” Quality Characteristics and Metrics related to M-Learning Process”, Amfiteatru Economic, Year XI, June 2009, No. 26, pp. 346-354. th Boja C., Batagan L., “Software Characteristics of M-Learning Applications”, Proc. of. 10 WSEAS International Conference on Mathematics and Computers in Business and Economics (MCBE'09), Prague, Czech Republic, ISSN: 1790-5109, ISBN: 978-960-474-063-5, March 23- 25, 2009, pp. 88-93. Praveena K., Betsy T., "Application of Cloud Computing in Academia," IUP Journal of Systems Management, vol. 7, no. 3, 2009, pp. 50–54. Ouf S., Nasr M., and Helmy Y., „An Enhanced E-Learning Ecosystem Based on an Integration between Cloud Computing and Web2.0”, Proc. IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2011, pp. 48-55.

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Gabor M., A. & Vasiu, R. Video data modelling based on the MPEG-7 standard in e-learning cloud computing. AWERProcedia Information Technology & Computer Science. [Online]. 2013, 04, pp 174-185. Available from: www.awer-center.org/pitcs

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