Automatic Multimedia Creation Enriched with Dynamic Conceptual Data

There is a growing gap between the multimedia production and the context centric multimedia services. The main problem is the under-exploitation of the content creation design. The idea is to support dynamic content generation adapted to the user or display profile. Our work is an implementation of a web platform for automatic generation of multimedia presentations based on SMIL (Synchronized Multimedia Integration Language) standard. The system is able to produce rich media with dynamic multimedia content retrieved automatically from different content databases matching the semantic context. For this purpose, we extend the standard interpretation of SMIL tags in order to accomplish a semantic translation of multimedia objects in database queries. This permits services to take benefit of production process to create customized content enhanced with real time information fed from databases. The described system has been successfully deployed to create advanced context centric weather forecasts

MASCOT: a mechanism for attention-based scale-invariant object recognition in images

The efficient management of large multimedia databases requires the development of new techniques to process, characterize, and search for multimedia objects. Especially in the case of image data, the rapidly growing amount of documents prohibits a manual description of the images’ content. Instead, the automated characterization is highly desirable to support annotation and retrieval of digital images. However, this is a very complex and still unsolved task. To contribute to a solution of this problem, we have developed a mechanism for recognizing objects in images based on the query by example paradigm. Therefore, the most salient image features of an example image representing the searched object are extracted to obtain a scale-invariant object model. The use of this model provides an efficient and robust strategy for recognizing objects in images independently of their size. Further applications of the mechanism are classical recognition tasks such as scene decomposition or object tracking in video sequences

Multimedia Database Systems and Oracle Video Streaming

The acquisition, generation, storage and processing of multimedia data incomputers and transmission over networks have grown tremendously in the recentpast. A multimedia database management system must support multimedia datatypes in addition to providing facilities for traditional DBMS function like databasecreation, data modeling, data retrieval, data access and organization, and dataindependence.In this paper we present several aspects of multimedia databases. Most ofthe classical database systems are extended to support the storage of multimediadata. This paper will focus on oracle database and in its datatypes for supporting thestorage and management of multimedia data, known as Intermedia. It is also shownthe architecture, configuration and implementation of video streaming. All themultimedia data are stored inside the oracle database so they will be undertransactional control. The implementation uses several tables to demonstrate all theprocess for doing it in the Oracle database with Helix server. We have created theprocedures in PL/SQL for inserting, retrieving the video and we have also used aPL/SQL package integrated with PHP script in order to help users to makesearching using keywords

Exploring a Quality of Service (QoS) Mechanism to Enhance Multimedia Database Query Processing in Wireless Mobile Environments.

Among the challenges of multimedia computing and mobile computing, a mechanism for data retrieval in multimedia databases under wireless mobile environments seems to be the most difficult issue. The problem is that sizes of images in a multimedia DBMS queried by mobile clients through wireless networks are different and unpredictable. Current Quality of Service (QoS) framework has no answer for it because all the QoS principles are based on users’ pre-requirements. However, the issue is that in multimedia applications, it is difficult to know the size of targeted retrieval object. There should be new mechanism of QoS to participate in query processing and provide an efficient theme around which mobile multimedia database applications can b practicably realized. In this thesis we focus on extending QoS management in wireless mobile environments to specify a range of acceptable QoS for multimedia query processing rather than trying either to guarantee specific values or to stop the querying. Through the investigation of current research approaches, we conclude that the statistical or empirical resource utilizations in query processing are the dominant methods to solve the problems. All proposals choose stopping query if the required QoS conditions can not meet the related statistical or empirical resources utilizations. To address QoS in mobile multimedia DBMS issues, we explore an approach to execute query processing based on real time QoS conditions all coming from client, network, and server. We propose a QoS-based matrix to support query processing of object-relational multimedia databases in the context of wireless mobile environments. The proposed QoS-based Querying Processing Precision Matrix (QQPPM) is based on (1) real-time QoS conditions in wireless networks; (2) multimedia database’s object properties; and (3) Mobile client-site data processing capability. We study related technologies as the foundations to support multimedia query processing in wireless mobile environments. Moreover, we conduct OPNET simulations, and the results indicate that our assumption is reasonable and practicable

The relationship between IR and multimedia databases

Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud \ud Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud \ud Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud \ud First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud \ud Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud \ud Third, we add the functionality to process the users' relevance feedback.\ud \ud We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud \ud We conclude with an outline for implementation of miRRor on top of the Monet extensible database system

Image mining: trends and developments

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining

Image mining: issues, frameworks and techniques

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. Despite the development of many applications and algorithms in the individual research fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper