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.

Experience of Animate Similarity Concepts in Multimedia Database

Abstract: This paper describes and reports an experience related to several applications of the Animate Vision Paradigm to query processing and indexing in multimedia databases. In particular we show as two main problems related to video and image databases may be usefully advantaged by an Animate Image Similarity model, namely the video segmentation problem and the image retrieval task in a Query By Example environment.

Similarity search and data mining techniques for advanced database systems.

Abstract: Modern automated methods for measurement, collection, and analysis of data in industry and science are providing more and more data with drastically increasing structure complexity. On the one hand, this growing complexity is justified by the need for a richer and more precise description of real-world objects, on the other hand it is justified by the rapid progress in measurement and analysis techniques that allow the user a versatile exploration of objects. In order to manage the huge volume of such complex data, advanced database systems are employed. In contrast to conventional database systems that support exact match queries, the user of these advanced database systems focuses on applying similarity search and data mining techniques. Based on an analysis of typical advanced database systems — such as biometrical, biological, multimedia, moving, and CAD-object database systems — the following three challenging characteristics of complexity are detected: uncertainty (probabilistic feature vectors), multiple instances (a set of homogeneous feature vectors), and multiple representations (a set of heterogeneous feature vectors). Therefore, the goal of this thesis is to develop similarity search and data mining techniques that are capable of handling uncertain, multi-instance, and multi-represented objects. The first part of this thesis deals with similarity search techniques. Object identification is a similarity search technique that is typically used for the recognition of objects from image, video, or audio data. Thus, we develop a novel probabilistic model for object identification. Based on it, two novel types of identification queries are defined. In order to process the novel query types efficiently, we introduce an index structure called Gauss-tree. In addition, we specify further probabilistic models and query types for uncertain multi-instance objects and uncertain spatial objects. Based on the index structure, we develop algorithms for an efficient processing of these query types. Practical benefits of using probabilistic feature vectors are demonstrated on a real-world application for video similarity search. Furthermore, a similarity search technique is presented that is based on aggregated multi-instance objects, and that is suitable for video similarity search. This technique takes multiple representations into account in order to achieve better effectiveness. The second part of this thesis deals with two major data mining techniques: clustering and classification. Since privacy preservation is a very important demand of distributed advanced applications, we propose using uncertainty for data obfuscation in order to provide privacy preservation during clustering. Furthermore, a model-based and a density-based clustering method for multi-instance objects are developed. Afterwards, original extensions and enhancements of the density-based clustering algorithms DBSCAN and OPTICS for handling multi-represented objects are introduced. Since several advanced database systems like biological or multimedia database systems handle predefined, very large class systems, two novel classification techniques for large class sets that benefit from using multiple representations are defined. The first classification method is based on the idea of a k-nearest-neighbor classifier. It employs a novel density-based technique to reduce training instances and exploits the entropy impurity of the local neighborhood in order to weight a given representation. The second technique addresses hierarchically-organized class systems. It uses a novel hierarchical, supervised method for the reduction of large multi-instance objects, e.g. audio or video, and applies support vector machines for efficient hierarchical classification of multi-represented objects. User benefits of this technique are demonstrated by a prototype that performs a classification of large music collections. The effectiveness and efficiency of all proposed techniques are discussed and verified by comparison with conventional approaches in versatile experimental evaluations on real-world datasets.

A Knowledge-based Multi-media Database System for Skeletal Radiology

Abstract: This paper describes our research toward the development a knowledge-based multimedia database system for bone age research. The prototype system is designed to allow users to query for information based on patient demographic profile, patient medical history, evolution constraints of the developing hand, image content, and fuzzy high-level terms and concepts. The major highlights of this work include: 1) object-oriented database management system, 2) graphical user interface for query formulation, 3) object-oriented data model which supports both traditional “Where-type” predicates and new object evolutionary and temporal predicates, and 4) cooperative query answering capabilities for intelligent query modification in the event of a null solution to the original query.

Multiware Database: A Distributed Object Database System for Multimedia Support

Abstract: This paper describes the Multiware Database, a database capable to store and manage complex multimedia documents using the features of both object database management systems and open distributed systems. This multimedia database is a component of the Multiware project1, a platform supporting distributed multimedia cooperative applications. Representation of multimedia documents in this system is normalized, in the sense that distinct aspects of documents, such as structural and exhibition aspects, are stored as distinct objects in the database. This normalization enables concise representations and integration of distinct multimedia standards within the same framework.

A Multimedia Database Server: Implementation and Functions.

Abstract: The paper presents the implementation and performances of a Multimedia Database Server software tool used for managing medium sized image collections. The software tool uses traditional data types (integer, double, char, varchar) and a new complex data type, called image. This type is used for storing the image along with the information extracted. This refers to the image’s size and type, color and texture characteristics. An element of originality for this software is the content based retrieval module that allows the user to build content based visual queries to the image level. The server’s performances are studied from the quality of the retrieval point of view. For the tests there are used both color and texture characteristics which are automatically extracted.