Flexible descriptor indexing for multimedia databases

Current multimedia applications require efficient tools for modeling and search. While solid models must support a large variety of concepts like sets, sub-sets, part-of hierarchies and integrate standards like Dublin Core, MPEG-7 (descriptors, descriptor schemes), TV-Anytime, MPEG-21 (digital item, digital item adaptation, digital item identification), the search algorithms must deal with heterogeneous multimedia data (text, image, audio and video). The search in audiovisual data requires the use of heterogeneous metadata representing a wide range of features, from low-level ones (color, motion) to high level ones (subject, mood), or domain-dependent concepts.The current work is part of the development of a prototype Multimedia Database and addresses the problem of choosing the proper storage and search strategy for the descriptors in the multimedia database. It includes a review of the main applicable data structures and search techniques and a case study from the point of view of our system requirements

Discovery and exploration using musicSpace

Musicologists have to rely upon an extraordinarily heterogeneous body of primary and secondary research sources, even when conducting the most basic exploratory research. Although increasingly available online, data is nevertheless routinely catalogued or stored in numerous discrete databases according to media type (text, image, video, audio) and historical period (contemporary literature/sources, historical literature/sources), yet most musicological research cuts across these artificial divisions; researching Monteverdi’s madrigals, for example, could involve performing essentially the same search several times, because there are several relevant data sources (RISM, Grove, Naxos, RILM, BL Integrated Catalogue and BL Sound Archive). The musicSpace project seeks to integrate access to musicological data sources by providing a single search interface, thereby removing the need for search repetition and reducing inefficiency. The vast increase in on-hand data that comes with database integration both demands and allows for the development of far more sophisticated, intelligent and interactive user interfaces. Accordingly, musicSpace facilitates searching and encourages browsing by displaying search results and parameters using multiple panes, allowing instantaneous paradigmatic shifts in search focus, and employing a detailed subject ontology to enable the semi-automatic construction of complex searches. In this paper we present the musicSpace explorer interface and demonstrate its efficacy. We describe key technologies behind musicSpace to reflect on performance and scalability. In particular, however, we describe how we will be evaluating the system in use for research, and describe our longitudinal study to assess the impact of this integrated approach on artefact discovery and research query support

EGO: a personalised multimedia management tool

The problems of Content-Based Image Retrieval (CBIR) sys- tems can be attributed to the semantic gap between the low-level data representation and the high-level concepts the user associates with images, on the one hand, and the time-varying and often vague nature of the underlying information need, on the other. These problems can be addressed by improving the interaction between the user and the system. In this paper, we sketch the development of CBIR interfaces, and introduce our view on how to solve some of the problems of the studied interfaces. To address the semantic gap and long-term multifaceted information needs, we propose a "retrieval in context" system. EGO is a tool for the management of image collections, supporting the user through personalisation and adaptation. We will describe how it learns from the user's personal organisation, allowing it to recommend relevant images to the user. The recommendation algorithm is detailed, which is based on relevance feedback techniques

A Benchmark for Image Retrieval using Distributed Systems over the Internet: BIRDS-I

The performance of CBIR algorithms is usually measured on an isolated workstation. In a real-world environment the algorithms would only constitute a minor component among the many interacting components. The Internet dramati-cally changes many of the usual assumptions about measuring CBIR performance. Any CBIR benchmark should be designed from a networked systems standpoint. These benchmarks typically introduce communication overhead because the real systems they model are distributed applications. We present our implementation of a client/server benchmark called BIRDS-I to measure image retrieval performance over the Internet. It has been designed with the trend toward the use of small personalized wireless systems in mind. Web-based CBIR implies the use of heteroge-neous image sets, imposing certain constraints on how the images are organized and the type of performance metrics applicable. BIRDS-I only requires controlled human intervention for the compilation of the image collection and none for the generation of ground truth in the measurement of retrieval accuracy. Benchmark image collections need to be evolved incrementally toward the storage of millions of images and that scaleup can only be achieved through the use of computer-aided compilation. Finally, our scoring metric introduces a tightly optimized image-ranking window.Comment: 24 pages, To appear in the Proc. SPIE Internet Imaging Conference 200

Building an Archive with Saada

Saada transforms a set of heterogeneous FITS files or VOTables of various categories (images, tables, spectra ...) in a database without writing code. Databases created with Saada come with a rich Web interface and an Application Programming Interface (API). They support the four most common VO services. Such databases can mix various categories of data in multiple collections. They allow a direct access to the original data while providing a homogenous view thanks to an internal data model compatible with the characterization axis defined by the VO. The data collections can be bound to each other with persistent links making relevant browsing paths and allowing data-mining oriented queries.Comment: 18 pages, 5 figures Special VO issu

Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline.

Modern computational neuroscience employs diverse software tools and multidisciplinary expertise to analyze heterogeneous brain data. The classical problems of gathering meaningful data, fitting specific models, and discovering appropriate analysis and visualization tools give way to a new class of computational challenges--management of large and incongruous data, integration and interoperability of computational resources, and data provenance. We designed, implemented and validated a new paradigm for addressing these challenges in the neuroimaging field. Our solution is based on the LONI Pipeline environment [3], [4], a graphical workflow environment for constructing and executing complex data processing protocols. We developed study-design, database and visual language programming functionalities within the LONI Pipeline that enable the construction of complete, elaborate and robust graphical workflows for analyzing neuroimaging and other data. These workflows facilitate open sharing and communication of data and metadata, concrete processing protocols, result validation, and study replication among different investigators and research groups. The LONI Pipeline features include distributed grid-enabled infrastructure, virtualized execution environment, efficient integration, data provenance, validation and distribution of new computational tools, automated data format conversion, and an intuitive graphical user interface. We demonstrate the new LONI Pipeline features using large scale neuroimaging studies based on data from the International Consortium for Brain Mapping [5] and the Alzheimer's Disease Neuroimaging Initiative [6]. User guides, forums, instructions and downloads of the LONI Pipeline environment are available at http://pipeline.loni.ucla.edu