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.

A Comprehensive Review on Content-Based Image Retrieval System: Features and Challenges

Abstract: Over a couple of years, huge attention is being paid by the researchers on the content-based image retrieval (CBIR) in order to successfully retrieve the contents from large-scale multimedia databases Typically, each day gigabytes of multimedia contents are being generated by the digital camera, cell phone, and PC and they are available in the form of multimedia database It is critical to find out the desired data from this vast collection of database CBIR is not only efficient in performing the image retrieval, but also organizes the common contents of a digital library in the indented database In this work, totally 25 research works are reviewed under CBIR techniques with respect to certain analytical views On the basis of different algorithmic models, they are categorized into transform-based CBIR technique, metaheuristic-based CBIR technique, learning-based CBIR technique, fuzzy-learning-based CBIR technique, and other CBIR techniques The analytical representations are defined by means of graphs and tabular columns Finally, a detailed description of research gaps and challenges is also presented under this scenario

Multimedia Database Retrieval

Abstract: This chapter presents machine learning methods for adaptive image retrieval. In a retrieval session, a nonlinear kernel is applied to measure image relevancy. Various new learning procedures are covered and applied specifically for adaptive image retrieval applications. These include the adaptive radial basis function (RBF) network, short term learning with the gradient-decent method, and the fuzzy RBF network. These methods constitute the likelihood estimation corresponding to visual content in a short-term relevance feedback (STRF). The STRF component can be further incorporated in a fusion module with contextual information in long-term relevance feedback (LTRF) using the Bayesian framework. This substantially increases retrieval accuracy.

A Fuzzy Conceptual Model for Multimedia Data with Application to News Video Domain

Abstract: The size of multimedia data is increasing fast due to the abundance of multimedia applications. Modeling the semantics of the data effectively is crucial for proper management of it. In this paper, we present a fuzzy conceptual data model for multimedia data which is also generic in the sense that it can be adapted to all multimedia domains. The model takes an object-oriented approach and it handles fuzziness at different representation levels where fuzziness is inherent in multimedia applications and should be properly modeled. The proposed model also has the nice feature of representing the structural hierarchy of multimedia data as well as the spatial and temporal relations of the data. The model is applied to the news video domain and implemented as a fuzzy multimedia database system where it turns out to be effective in representing this domain and thereby provides an evidence for the general applicability of the model.

The Teradata SQL3 Multimedia Database Server

Abstract: Multimedia applications??-??such as fingerprint matching, signature verification, face recognition, and speech recognition or translation??-??require complex abstract data-type support within database management systems. However, conventional databases are not designed to support multimedia. In this chapter, we describe several multimedia database challenges and explain how Teradata solves these problems with its SQL3 Multimedia Database system. A key component of this system is the Multimedia Object Manager, a general-purpose content-based multimedia server designed for the symmetric multiprocessor (SMP) and massively parallel processor (MPP) environments. The Teradata SQL3 Multimedia Database system allows users to define and manipulate user-defined functions (UDFs), which are invoked in parallel in the Multimedia Object Manager to analyze/manipulate the contents of multimedia objects. The two key characteristics of this subsystem are its support for content-based retrieval and multimodal integration. We provide an in-depth analysis of retrieval techniques using feature extraction and spatial indices. We also illustrate the power of multimodal integration by walking through the development of a complex application involving the generation of a ?>talking agent, ?> which uses speech, image, and video data types within the database system.