ADAMpro: Database Support for Big Multimedia Retrieval

For supporting retrieval tasks within large multimedia collections, not only the sheer size of data but also the complexity of data and their associated metadata pose a challenge. Applications that have to deal with big multimedia collections need to manage the volume of data and to effectively and efficiently search within these data. When providing similarity search, a multimedia retrieval system has to consider the actual multimedia content, the corresponding structured metadata (e.g., content author, creation date, etc.) and—for providing similarity queries—the extracted low-level features stored as densely populated high-dimensional feature vectors. In this paper, we present ADAM pro , a combined database and information retrieval system that is particularly tailored to big multimedia collections. ADAM pro follows a modular architecture for storing structured metadata, as well as the extracted feature vectors and it provides various index structures, i.e., Locality-Sensitive Hashing, Spectral Hashing, and the VA-File, for a fast retrieval in the context of a similarity search. Since similarity queries are often long-running, ADAM pro supports progressive queries that provide the user with streaming result lists by returning (possibly imprecise) results as soon as they become available. We provide the results of an evaluation of ADAM pro on the basis of several collection sizes up to 50 million entries and feature vectors with different numbers of dimensions

Computer animation data management: Review of evolution phases and emerging issues

The computer animation industry has been booming and prospering in recent thirty years. One of the significant changes faced by this industry is the evolution of computer-animation data and, yet, extant literature has offered very little insights into the evolution process and management issues pertinent to computer-animation data. Hence, many questions have surfaced in the extant literature of computer-animation data management. For example, to what extent has the data content expanded in terms of quantity and quality? To what extent has the information technology used to store and process the data changed? To what extent have the user and the community groups diversified in terms of their nature and number? Knowledge pertaining to these issues can provide new research directions to academics and also insights to practitioners for more effective and innovative management of computer-animation data. This conceptual paper, therefore, takes the pioneering step to address these issues by proposing four factors prudent for examining the evolution phases associated with computer-animation data management: technology, content, users, and community. Next, this paper presents a conceptual framework illustrating the inter-dependent relationships between these four factors together with associated theoretical and managerial issues. This paper, albeit limited by its conceptual nature, advances the extant literature of computer animation, information system, and open-product model

Modellgetriebene Entwicklung inhaltsbasierter Bildretrieval-Systeme auf der Basis von objektrelationalen Datenbank-Management-Systeme

In this thesis, the model-driven software development paradigm is employed in order to support the development of Content-based Image Retrieval Systems (CBIRS) for different application domains. Modeling techniques, based on an adaptable conceptual framework model, are proposed for deriving the components of a concrete CBIRS. Transformation techniques are defined to automatically implement the derived application specific models in an object-relational database management system. A set of criteria assuring the quality of the transformation are derived from the theory for preserving information capacity applied in database design.In dieser Dissertation wird das Paradigma des modellgetriebenen Softwareentwurfs für die Erstellung von inhaltsbasierten Bildretrieval-Systemen verwendet. Ein adaptierbares Frameworkmodell wird für die Ableitung des Modells eines konkreten Bildretrieval-Systems eingesetzt. Transformationstechniken für die automatische Generierung von Implementierungen in Objektorientierten Datenbank-Management-Systemen aus dem konzeptuellen Modell werden erarbeitet. Die aus der Theorie des Datenbankentwurfs bekannten Anforderungen zur Kapazitätserhaltung der Transformation werden verwendet, um Kriterien für die erforderliche Qualität der Transformation zu definieren

Database support for large-scale multimedia retrieval

With the increasing proliferation of recording devices and the resulting abundance of multimedia data available nowadays, searching and managing these ever-growing collections becomes more and more difficult. In order to support retrieval tasks within large multimedia collections, not only the sheer size, but also the complexity of data and their associated metadata pose great challenges, in particular from a data management perspective. Conventional approaches to address this task have been shown to have only limited success, particularly due to the lack of support for the given data and the required query paradigms. In the area of multimedia research, the missing support for efficiently and effectively managing multimedia data and metadata has recently been recognised as a stumbling block that constraints further developments in the field. In this thesis, we bridge the gap between the database and the multimedia retrieval research areas. We approach the problem of providing a data management system geared towards large collections of multimedia data and the corresponding query paradigms. To this end, we identify the necessary building-blocks for a multimedia data management system which adopts the relational data model and the vector-space model. In essence, we make the following main contributions towards a holistic model of a database system for multimedia data: We introduce an architectural model describing a data management system for multimedia data from a system architecture perspective. We further present a data model which supports the storage of multimedia data and the corresponding metadata, and provides similarity-based search operations. This thesis describes an extensive query model for a very broad range of different query paradigms specifying both logical and executional aspects of a query. Moreover, we consider the efficiency and scalability of the system in a distribution and a storage model, and provide a large and diverse set of index structures for high-dimensional data coming from the vector-space model. Thee developed models crystallise into the scalable multimedia data management system ADAMpro which has been implemented within the iMotion/vitrivr retrieval stack. We quantitatively evaluate our concepts on collections that exceed the current state of the art. The results underline the benefits of our approach and assist in understanding the role of the introduced concepts. Moreover, the findings provide important implications for future research in the field of multimedia data management

Searching images by color in multimedia database systems.

This dissertation presents several tasks that have been completed in order to achieve the above goal. First, this dissertation presents algorithms for processing color-based queries based on the colors contained within an image. They process queries of the type "Identify all images that are between PCTmin and PCTmax percent of color CQ", where PCTmin and PCTmax represent percentages and C Q represents a color in the RGB (Red, Green, Blue) model.Third, this dissertation proposes a data structure for organizing virtual images identifiers stored in the MMDBMS in order to reduce the amount of time it takes to process the above algorithm. By using the data structure, the system will be able to identify some of the virtual images that can satisfy a given query without analyzing their sequences of editing operations. The reduction in the query processing time occurs from the reduction in the number of virtual images that have to be analyzed.Next, this dissertation proposes algorithms for measuring the similarity between two images when one of them is stored virtually, where the similarity is based on the colors contained within an image. This allows an MMDBMS to process color-based searching queries of the type "Identify the k images that most resemble Q based on color", where k represents the desired number of images, and Q represents a query object by providing a method to measure how similar each virtual image is to the query object.Previous research has demonstrated that instead of storing images in a Multimedia DataBase Management System (MMDBMS) using a conventional binary format, space can be saved by storing some of the images virtually, meaning that they are stored as sequences of editing operations. Since the existing techniques for searching images by color typically assume that the images are stored in conventional binary formats, new techniques and strategies for processing the queries are needed when the images are stored virtually. The goal of this dissertation is to develop techniques for performing color-based searches of virtual images and determine their strengths and weaknesses.Finally, this dissertation constructs a prototype system to compare the above algorithms to the conventional approach for processing color-based search queries that use images stored as binary objects. The performance evaluation is based on permanent storage space used, color-based search query processing time, insertion query processing time, as well as accuracy. The comparison results show that unlike the alternative approaches, the proposed algorithms are able to perform efficiently in both searching and insertion time while still saving storage space through the use of virtual images