Database technology and the management of multimedia data in Mirror

Multimedia digital libraries require an open distributed architecture instead of a monolithic database system. In the Mirror project, we use the Monet extensible database kernel to manage different representations of multimedia objects. To maintain independence between content, meta-data, and the creation of meta-data, we allow distribution of data and operations using CORBA. This open architecture introduces new problems for data access. From an end user’s perspective, the problem is how to search the available representations to fulfill an actual information need; the conceptual gap between human perceptual processes and the meta-data is too large. From a system’s perspective, several representations of the data may semantically overlap or be irrelevant. We address these problems with an iterative query process and active user participation through relevance feedback. A retrieval model based on inference networks assists the user with query formulation. The integration of this model into the database design has two advantages. First, the user can query both the logical and the content structure of multimedia objects. Second, the use of different data models in the logical and the physical database design provides data independence and allows algebraic query optimization. We illustrate query processing with a music retrieval application

Knowledge-based document filing for texpros

This dissertation presents a knowledge-based document filing system for TEXPROS. The requirements of a. personal document processing system are investigated. In order for the system to be used in various application domains, a flexible, dynamic modeling approach is employed by getting the user involved in document modeling. The office documents are described using a dual-model which consists of a document type hierarchy and a folder organization. The document type hierarchy is used to capture the layout, logical and conceptual structures of documents. The folder organization, which is defined by the user, emulates the real world structure for organizing and storing documents in an office environment. The document filing and retrieval are predicate-driven. The user can specify filing criteria and queries in terms of predicates. The predicate specification and folder organization specification are described. It is shown that the new specifications can prevent false drops which happen in the previous approach. The dual models are incorporated by a three-level storage architecture. This storage architecture supports efficient document and information retrieval by limiting the searches to those frame instances of a document type within those folders which appear to be the most similar to the corresponding queries, Specifically, a. three-level retrieval strategy is used in document and information retrieval. Firstly, a knowledge-based query preprocess is applied for efficiently reducing the search space to a small set of frame instances, using the information in the query formula. Secondly, the knowledge and content-based retrieval on the small set of frame instances is applied. Finally, the third level storage provides a platform for adopting potential content-based multimedia document retrieval techniques. A knowledge-based predicate evaluation engine is described for automating document filing. The dissertation presents a knowledge representation model. The knowledge base is dynamicly created by a learning agent, which demonstrates that the notion of flexible and dynamic modeling is applicable. The folder organization is implemented using an agent-based architecture. Each folder is monitored by a filing agent. The basic operations for constructing and reorganizing a folder organization are defined. The dissertation also discusses the cooperation among the filing agents, which is needed for implementing the folder organization

Exploiting multimedia content : a machine learning based approach

Advisors: Prof. M Gopal, Prof. Santanu Chaudhury. Date and location of PhD thesis defense: 10 September 2013, Indian Institute of Technology DelhiThis thesis explores use of machine learning for multimedia content management involving single/multiple features, modalities and concepts. We introduce shape based feature for binary patterns and apply it for recognition and retrieval application in single and multiple feature based architecture. The multiple feature based recognition and retrieval frameworks are based on the theory of multiple kernel learning (MKL). A binary pattern recognition framework is presented by combining the binary MKL classifiers using a decision directed acyclic graph. The evaluation is shown for Indian script character recognition, and MPEG7 shape symbol recognition. A word image based document indexing framework is presented using the distance based hashing (DBH) defined on learned pivot centres. We use a new multi-kernel learning scheme using a Genetic Algorithm for developing a kernel DBH based document image retrieval system. The experimental evaluation is presented on document collections of Devanagari, Bengali and English scripts. Next, methods for document retrieval using multi-modal information fusion are presented. Text/Graphics segmentation framework is presented for documents having a complex layout. We present a novel multi-modal document retrieval framework using the segmented regions. The approach is evaluated on English magazine pages. A document script identification framework is presented using decision level aggregation of page, paragraph and word level prediction. Latent Dirichlet Allocation based topic modelling with modified edit distance is introduced for the retrieval of documents having recognition inaccuracies. A multi-modal indexing framework for such documents is presented by a learning based combination of text and image based properties. Experimental results are shown on Devanagari script documents. Finally, we have investigated concept based approaches for multimedia analysis. A multi-modal document retrieval framework is presented by combining the generative and discriminative modelling for exploiting the cross-modal correlation between modalities. The combination is also explored for semantic concept recognition using multi-modal components of the same document, and different documents over a collection. An experimental evaluation of the framework is shown for semantic event detection in sport videos, and semantic labelling of components of multi-modal document images

The relationship between IR and multimedia databases

Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud \ud Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud \ud Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud \ud First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud \ud Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud \ud Third, we add the functionality to process the users' relevance feedback.\ud \ud We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud \ud We conclude with an outline for implementation of miRRor on top of the Monet extensible database system

Requirements for an Adaptive Multimedia Presentation System with Contextual Supplemental Support Media

Investigations into the requirements for a practical adaptive multimedia presentation system have led the writers to propose the use of a video segmentation process that provides contextual supplementary updates produced by users. Supplements consisting of tailored segments are dynamically inserted into previously stored material in response to questions from users. A proposal for the use of this technique is presented in the context of personalisation within a Virtual Learning Environment. During the investigation, a brief survey of advanced adaptive approaches revealed that adaptation may be enhanced by use of manually generated metadata, automated or semi-automated use of metadata by stored context dependent ontology hierarchies that describe the semantics of the learning domain. The use of neural networks or fuzzy logic filtering is a technique for future investigation. A prototype demonstrator is under construction

Towards an All-Purpose Content-Based Multimedia Information Retrieval System

The growth of multimedia collections - in terms of size, heterogeneity, and variety of media types - necessitates systems that are able to conjointly deal with several forms of media, especially when it comes to searching for particular objects. However, existing retrieval systems are organized in silos and treat different media types separately. As a consequence, retrieval across media types is either not supported at all or subject to major limitations. In this paper, we present vitrivr, a content-based multimedia information retrieval stack. As opposed to the keyword search approach implemented by most media management systems, vitrivr makes direct use of the object's content to facilitate different types of similarity search, such as Query-by-Example or Query-by-Sketch, for and, most importantly, across different media types - namely, images, audio, videos, and 3D models. Furthermore, we introduce a new web-based user interface that enables easy-to-use, multimodal retrieval from and browsing in mixed media collections. The effectiveness of vitrivr is shown on the basis of a user study that involves different query and media types. To the best of our knowledge, the full vitrivr stack is unique in that it is the first multimedia retrieval system that seamlessly integrates support for four different types of media. As such, it paves the way towards an all-purpose, content-based multimedia information retrieval system

The Mirror MMDBMS architecture

Handling large collections of digitized multimedia data, usually referred to as multimedia digital libraries, is a major challenge for information technology. The Mirror DBMS is a research database system that is developed to better understand the kind of data management that is required in the context of multimedia digital libraries (see also URL http://www.cs.utwente.nl/~arjen/mmdb.html). Its main features are an integrated approach to both content management and (traditional) structured data management, and the implementation of an extensible object-oriented logical data model on a binary relational physical data model. The focus of this work is aimed at design for scalability

An architecture for life-long user modelling

In this paper, we propose a united architecture for the creation of life-long user profiles. Our architecture combines different steps required for a user prole, including feature extraction and representation, reasoning, recommendation and presentation. We discuss various issues that arise in the context of life-long profiling