Multimedia database

About: Multimedia database is a research topic. Over the lifetime, 1404 publications have been published within this topic receiving 19856 citations. The topic is also known as: Multimedia database & MMDB.

Query Languages in Multimedia Database Systems

Abstract: Declarative query languages are an important feature of database management systems and have played an important role in their success. As database management technology enters the multimedia information system area, the availability of special-purpose query languages for multimedia applications will be equally important. In this report 1 , we survey multimedia query languages and query models. Particularly, we look at those systems from the point of view of well-deened queries, fuzzy queries, visual queries, and query presentations. Several research issues, such as generic multimedia query languages, incremental queries, fuzzy queries, spatio-temporal queries, feature storage and organization, are also identiied. In our opinion these are vital issues for the success and development of a multimedia query language.

Cognitive View Mechanism for Content-based Multimedia Information Retrieval

Abstract: This paper describes a cognitive human interface for visual interaction with multimedia database systems Our approach gives a general framework of visual interaction We adopt both an image model and a user model to interpret and operate the contents of visual information from user’s viewpoint The image model describes the physical constraints of image data, while the user model reflects the visual perception processes of the user

MMIR: An Advanced Content-Based Image Retrieval System Using a Hierarchical Learning Framework

Abstract: This chapter introduces an advanced content-based image retrieval (CBIR) system, MMIR, where Markov model mediator (MMM) and multiple instance learning (MIL) techniques are integrated seamlessly and act coherently as a hierarchical learning engine to boost both the retrieval accuracy and efficiency. It is well-understood that the major bottleneck of CBIR systems is the large semantic gap between the low-level image features and the highlevel semantic concepts. In addition, the perception subjectivity problem also challenges a CBIR system. To address these issues and challenges, the proposed MMIR system utilizes the MMM mechanism to direct the focus on the image level analysis together with the MIL technique (with the neural network technique as its core) to real-time capture and learn the object-level semantic concepts with some help of the user feedbacks. In addition, from a long-term learning perspective, the user feedback logs are explored by MMM to speed up the learning process and to increase the retrieval accuracy for a query. The comparative studies on a large set of real-world images demonstrate the promising performance of our proposed MMIR system. IGI PUBLISHING This paper appears in the publication, Advances in Machine Learning Applications in Software Engineering edited by Du Zhang & Jeffrey J.P. Tsa © 2007, IGI Global 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.igi-pub.com ITB14911

Managing large-scale multimedia repositories

Abstract: Capturing and organizing vast volumes of images, such as scientific and medical data, requires new information processing techniques for context of pattern recognition and data mining. In content based retrieval, the main task is the seeking of entries in an image database that are most similar, in some sense, to a given query object. The volume of the data is large, and the feature vectors are, typically, of high dimensionality. In high dimensions, the curse of dimensionality is an issue as the search space grows exponentially with the dimensions. In addition, it is impractical to store all the extracted feature vectors from millions of images in main memory. The time spent accessing the feature vectors on hard storage devices overwhelmingly dominates the time complexity of the search. The time complexity problem is further emphasized when the search is to be performed multiple times in an interactive scenario. One of the main contributions of this dissertation is to enable efficient, effective, and interactive data access. We introduce a modified texture descriptor that has comparable performance but nearly half the dimensionality and less computational expense. Moreover, based on the statistical properties of the texture descriptors, we propose an adaptive for approximate nearest neighbor search indexing approach. In content-based retrieval systems, exact search and retrieval in the feature space is often wasteful. We present an approximate similarity search method for large feature datasets. It improves similarity retrieval efficiency without compromising on the retrieval quality. We also address the computation bottleneck of a real-life system interface. We propose a similarity search scheme that exploits correlations between two consecutive nearest neighbor sets and considerably accelerates interactive search, particularly in the context of relevance feedback mechanisms that support distance metric update approach. In multimedia query processing, the main task is the seeking of entries in a multimedia database that are most similar to a given query object. Since feature descriptors approximately capture information contained in images, they often do not capture visual concepts contained in those images. Semantic analysis of multimedia content is needed. We introduce a framework for learning and summarizing basic semantic concepts in scientific datasets. Moreover, we present a method to detect coarse spatial patterns and visual concepts in image and video datasets. Experiments on a large set of aerial images and video data are presented.

Management of multimedia data using an object-oriented database system

Abstract: A multimedia database management system (MMDBMS) must provide facilities to store, model and query multimedia data. In the context of the STORM (Structural and Temporal Object-oRiented Multimedia database system) project, we are developing an object-oriented client-server MMDBMS which addresses these problems. We use an object-oriented approach to develop reusable components which can be easily integrated in the general framework of multimedia database applications. In this paper, we present the main functionalities of our system and we focus on video objects and their integration in multimedia presentations.