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.

Optimal k-Nearest-Neighbor Query Processing via Multiple Lower Bound Approximations

Abstract: Given a very large multimedia database, how to process k-nearest-neighbor queries efficiently? While the sequential scan is one of the most obvious solutions for small-to-moderate multimedia databases, it becomes practically infeasible when the database size grows. Concomitant with the volume and velocity of data, multimedia databases are frequently endowed with a complex distance-based similarity model that supports content-based data access in an adjustable and adaptive manner. Typical for many state-of-the-art distance-based similarity models is an at least quadratic computation time complexity for a single distance evaluation between two multimedia objects. Thus the search for the most query-like multimedia objects is still one of the major challenges.In this paper, we address the problem of optimal k-nearest-neighbor query processing via multiple lower bound approximations in very large multimedia databases. To this end, we propose the concepts of filter-optimality and refinement-optimality and present the Cascading Multi-Step Algorithm and the Interleaved Multi-Step Algorithm for fast query processing. Besides the algorithms’ properties, we study their query processing performance with respect to the number of CPU and I/O operations on large-scale benchmark multimedia databases. Our performance analysis shows how to process k-nearest-neighbor queries in multimedia databases efficiently and provides a guide for further research.

A Multiple-Instance Learning Based Approach to Multimodal Data Mining

Abstract: This paper presents multiple-instance learning based approach to multimodal data mining in a multimedia database. This approach is a highly scalable and adaptable framework that the authors call co-learning. Theoretic analysis and empirical evaluations demonstrate the advantage of the strong scalability and adaptability. Although this framework is general for multimodal data mining in any specific domain, to evaluate this framework, the authors apply it to the Berkeley Drosophila ISH embryo image database for the evaluations of the mining performance in comparison with a state-of-the-art multimodal data mining method to showcase the promise of the co-learning framework.

A Machine Learning-Based Model for Content-Based Image Retrieval

Abstract: A multimedia index makes it possible to group data according to similarity criteria. Traditional index structures are based on trees and use the k-Nearest Neighbors (k-NN) approach to retrieve databases. Due to some disadvantages of such an approach, the use of neighborhood graphs was proposed. This approach is interesting, but it has some disadvantages, mainly in its complexity. This chapter presents a step in a long process of analyzing, structuring, and retrieving multimedia databases. Indeed, we propose an effective method for locally updating neighborhood graphs, which constitute our multimedia index. Then, we exploit this structure in order to make the retrieval process easy and effective, using queries in an image form in one hand. In another hand, we use the indexing structure to annotate images in order to describe their semantics. The proposed approach is based on an intelligent manner for locating points in a multidimensional space. Promising results are obtained after experimentations on various databases. Future issues of the proposed approach are very relevant in this domain. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.irm-press.com ITB13860 IRM PRESS This chapter appears in the book, Semantic-Based Visual Information Retrieval by Y.J. Zhang © 2007, Idea Group Inc. A Machine Learning-Based Model for Content-Based Retrieval Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Introduction Data interrogation is a fundamental problem in various scientific communities. The database and statistics communities (with their various fields such as data mining) are certainly the most implied. Each community considers the interrogation from a different point of view. The database community, for example, deals with great volumes of data by organizing them in an adequate structure in order to be able to answer queries in the most effective way. This is done by using index structures. The statistics community deals only with data samples in order to produce predictive models that are able to draw conclusions on phenomena; these conclusions then are generalized to the whole items. This is achieved using various structures such as decision trees (Mitchell, 2003), Kohonen maps (Kohonen, 2001), neighborhood graphs (Toussaint, 1991), and so forth. Dealing with multimedia databases means dealing with content-based retrieval. There are two fundamental problems associated with content-based retrieval systems: (a) how to specify a query and (b) how to access the intended data efficiently for a given query. The main objective is to capture the semantics of the considered data. For traditional database systems, the semantics of content-based access are finding data items that are match exactly the specified keywords in queries. For multimedia database systems, both query specification and data access become much harder (Chiueh, 1994). To give the computer the ability to mimic the human being in scene analysis needs to explicit the process by which it moves up from the low level to the highest one. Multimedia processing tools give many ways to transform an image/video into a vector. For instance, MPEG-7 protocol associates a set of quantitative attributes to each image/video. The computation of these features is integrated and automated fully in many software platforms. In return, the labels basically are given by the user, because they are issued from the human language. The relevance of the image/video retrieval process depends on the vector of characteristics. Nevertheless, if we assume that the characteristics are relevant in the representation space, that it is supposed to be Rp, the images that are neighbors should have very similar meanings. In order to perform an interrogation in a multimedia database, it must be structured in an adequate way. For that, an index is used. Indexing a multimedia database consists of finding a way to structure the data so that the neighbors of each multimedia document can be located easily according to one or more similarity criteria. The index structures used in databases are generally in a tree form and aim to create clusters, which are represented by the sheets of the tree and contain rather similar documents. However, in addition to the fact that a traditional index cannot support data with dimensions higher than 16, dealing with multimedia databases needs more operations such as classification and annotation. This is why the use of models issued from the automatic learning community can be very helpful. The rest of this chapter is organized as follows. The next section introduces the point location and the database indexing problems. Section 3 presents the motivation and the contributions of our work. Section 4 describes the neighborhood graphs that are the foundation of this contribution. Our contributions are addressed in Section 5. The indexing method and the optimization of the neighborhood graphs are discussed in Section 5.1. Semi-automatic annotation is discussed in Section 5.2. Section 6 gives some experiments that were performed in order to evaluate and validate our approach. We conclude and give some future issues in Section 7. 20 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the publisher's webpage: www.igi-global.com/chapter/machine-learning-based-modelcontent/28929

Design and model for a computer supported cooperative environment for CASE and CAD applications

Abstract: For the past decade, numerous commercial and experimental systems have been designed for collaborative writing applications. From our experience working in the area of CSCW research and development, we have come to realize the possibility and the potential significance of CSCW systems in the engineering design process. The objective of the research is to introduce and develop a CSCW environment to support engineering design, specifically, in a distributed environment that combines computer aided design (CAD) and computer aided software engineering (CASE). In an effort to combine them into a seamless engineering package, we first target supporting engineering design (using CAD) with computer simulation (using CASE). This paper addresses issues involved in the design and development of the environment, including the graphical user interface, text and graphics editors, video and audio communication, multimedia database, document integration, and software simulation.

Multimedia database retrieval using hand-drawn sketches

Abstract: We propose an integrated approach to color and shape based retrieval which enables the user to search a color image database intuitively by presenting simple sketches. Each of the images is represented by a Hidden Markov Model (HMM) which has been concatenated with modified filler models in order to obtain scale and rotation invariance properties. These properties are particularly important when using query by sketch due to the skew which occurs naturally in human handwriting and in drawings. The use of streams (sets of features that are assumed to be statistically independent) within the HMM framework allows the integration of shape and color derived features into a single model, thereby allowing one to control the influence of the different streams via stream weights. The approach has been evaluated on a color image database containing 120 different isolated objects with arbitrary orientation and showed good retrieval results with several users. Furthermore, the use of HMMs allows efficient pruning and thus a fast retrieval even with large databases.