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

Automating the construction of scene classifiers for content-based video retrieval

This paper introduces a real time automatic scene classifier within content-based video retrieval. In our envisioned approach end users like documentalists, not image processing experts, build classifiers interactively, by simply indicating positive examples of a scene. Classification consists of a two stage procedure. First, small image fragments called patches are classified. Second, frequency vectors of these patch classifications are fed into a second classifier for global scene classification (e.g., city, portraits, or countryside). The first stage classifiers can be seen as a set of highly specialized, learned feature detectors, as an alternative to letting an image processing expert determine features a priori. We present results for experiments on a variety of patch and image classes. The scene classifier has been used successfully within television archives and for Internet porn filtering

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

Empirical Results on Interactive E-learning Using Knowledge Acquisition Based Learning

This paper presents empirical results on the efficiency of e-Learning systems which deploy and use knowledge acquisition based method (KA-LMS) for enhancing the learning capabilities of students. A new e-Learning method, which was developed by the author, is used to measure the impact of the new method on the learning achievements of the students. The method utilizes learning management systems, which restricts the ability of a learning student to advance from one topic to the next one unless he/she has acquired a minimum set of learning outcomes and knowledge. The data is collected from relatively large class rooms, where students attend online classes using the knowledge acquisition based method, and then the same set of students go through physical face to face exams. The results show that on the average students were able to score in the physical exam similar or higher grades compared to the results obtained automatically using the e-Learning KA-LMS. The effectiveness of KA-LMS was shown to be effective during the Covid-19 lockdown

Automatic Image Annotation using 2D MHMM

Automated Image Annotation is an important and challenging task in the field of computer vision and CBIR(Content-Based Image Retrieval). It has extensive use in research as well as personal fields. In this project, the same has been achieved with the help of a statistical method, namely a 2-dimensional multi-resolution hidden Markov model. Prior to classifying images by the system, it is trained using a set of images which are previously annotated using labels. Then the image to be annotated is compared against each trained model produced as a result of the previous step. This produces a parameter called likelihood. The label having the highest likelihood is assigned to the image

GAT, a Graphical Annotation Tool for semantic regions

The original publication is available at http://www.springerlink.com/content/j78782k762617352/This article presents GAT, a Graphical Annotation Tool based on a region-based hierarchical representation of images. The proposed solution uses Partition Trees to navigate through the image segments which are automatically defined at different spatial scales. Moreover, the system focuses on the navigation through ontologies for a semantic annotation of objects and of the parts that compose them. The tool has been designed under usability criteria to minimize the user interaction by trying to predict the future selection of regions and semantic classes. The implementation uses MPEG-7/XML input and output data to allow interoperability with any type of Partition Tree. This tool is publicly available and its source code can be downloaded under a free software license.Peer ReviewedPostprint (published version