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.

Video Database Modeling and Temporal Pattern Retrieval using Hierarchical Markov Model Mediator

Abstract: The dream of pervasive multimedia retrieval and reuse will not be realized without incorporating semantics in the multimedia database. As video data is penetrating many information systems, the need for database support for video data evolves. Hence, we propose an innovative database modeling mechanism called Hierarchical Markov Model Mediator (HMMM) which integrates lowlevel features, semantic concepts, and high-level user perceptions for modeling and indexing multiple-level video objects to facilitate temporal pattern retrieval. Different from the existing database modeling methods, our approach carries a stochastic and dynamic process in both search and similarity calculation. In the retrieval of semantic event patterns, HMMM always tries to traverse the right path and therefore it can assist in retrieving more accurate patterns quickly with lower computational costs. Moreover, HMMM supports feedbacks and learning strategies, which can proficiently assure the continuous improvements of the overall performance.

SOM-based R*-tree for similarity retrieval

Abstract: Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (e.g., documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors. A feature vector is a vector that represents a set of features, and are usually high-dimensional data. The performance of conventional multidimensional data structures (e.g., R-tree family K-D-B tree, grid file, TV-tree) tends to deteriorate as the number of dimensions of feature vectors increases. The R*-tree is the most successful variant of the R-tree. We propose a SOM-based R*-tree as a new indexing method for high-dimensional feature vectors. The SOM-based R*-tree combines SOM and R*-tree to achieve search performance more scalable to high dimensionalities. Self-organizing maps (SOMs) provide mapping from high-dimensional feature vectors onto a two-dimensional space. The mapping preserves the topology of the feature vectors. The map is called a topological feature map, and preserves the mutual relationships (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. We experimentally compare the retrieval time cost of a SOM-based R*-tree with that of an SOM and an R*-tree using color feature vectors extracted from 40,000 images.

Media processing and retrieval model for multimedia documents databases

Abstract: Typically, multimedia applications involve thousands of hours of video, images, audio, text and graphics that need to be stored, retrieved and manipulated in a large multimedia database. There is therefore an important need for novel techniques and systems which provide an efficient retrieval facility of the voluminous information stored in the multimedia database. Such a facility will consist of a query language, a query user interface, a data structure, indexing and finally content searching algorithms. Our interests focus on the first four components of such system. In this thesis, we propose a multimedia query specification language that can be used to fully describe a multimedia segment that needs to be received from a database. We designed an object-oriented database schema that can integrate the logical, temporal and spatial information of a multimedia document. We also proposed an algorithm to index a video by detecting camera breaks and coarse camera operations which will then be used as indices. Moreover, we discussed how to index interactive multimedia documents where undetermined user actions are involved, by proposing a temporal model that has the capability to represent interactive multimedia document scenarios. Indices that represent possible user actions and reactions are then extracted from the model and used to index such documents. Finally, we designed a simple and friendly query user interface on top of the proposed language, in which users can fully describe graphically a multimedia segment.