Analyzing the efficacy of using digital ink devices in a learning environment

There has been increased interest on the impact of mobile devices such as PDAs and Tablet PCs in introducing new pedagogical approaches and active learning experiences. We propose an intelligent system that efficiently addresses the inherent subjectivity in student perception of note taking and information retrieval. We employ the idea of cross indexing the digital ink notes with matching electronic documents in the repository. Latent Semantic Indexing is used to perform document and page level indexing. Thus for each retrieved document, the user can go over to the relevant pages that match the query. Techniques to handle problems such as polysemy (multiple meanings of a word) in large databases, document folding and no match for query are discussed. We tested our system for its performance, usability and effectiveness in the learning process. The results from the exploratory studies reveal that the proposed system provides a highly enhanced student learning experience, thereby facilitating high test scores.

Optimal quadtrees for image segments

Quadtrees are compact hierarchical representations of images. In this paper, we define the efficiency of quadtrees in representing image segments and derive the relationship between the size of the enclosing rectangle of an image segment and its optimal quadtree. We show that if an image segment has an enclosing rectangle having sides of lengths x and y, such that 2N-1 × max (x, y) ? 2N, then the optimal quadtree may be the one representing an image of size 2N × 2N or 2N+1 × 2N+1. It is shown that in some situations the quadtree corresponding to the larger image has fewer nodes. Also, some necessary conditions are derived to identify segments for which the larger image size results in a quadtree which is no more expensive than the quadtree for the smaller image size.     

Narrowing the semantic gap - improved text-based web document retrieval using visual features

We present the results of our work that seek to negotiate the gap between low-level features and high-level concepts in the domain of web document retrieval. This work concerns a technique, called the latent semantic indexing (LSI), which has been used for textual information retrieval for many years. In this environment, LSI determines clusters of co-occurring keywords so that a query which uses a particular keyword can then retrieve documents perhaps not containing this keyword, but containing other keywords from the same cluster. In this paper, we examine the use of this technique for content-based web document retrieval, using both keywords and image features to represent the documents. Two different approaches to image feature representation, namely, color histograms and color anglograms, are adopted and evaluated. Experimental results show that LSI, together with both textual and visual features, is able to extract the underlying semantic structure of web documents, thus helping to improve the retrieval performance significantly, even when querying is done using only keywords.     

Information retrieval on the World Wide Web

Effective search and retrieval are enabling technologies for realizing the full potential of the Web. The authors examine relevant issues, including methods for representing document content. They also compare available search tools and suggest methods for improving retrieval effectiveness     

A pictorial index mechanism for model-based matching

We are currently developing unified query processing strategies for image databases. To perform this task, model-based representations of images by content are being used, as well as a hierarchical generalization of a relatively new object-recognition technique called data-driven indexed hypotheses. As the name implies, it is index-based, from which its efficiency derives. Earlier approaches to data-driven model-based object recognition techniques were not capable of handling complex image data containing overlapping, partially visible, and touching objects due to the limitations of the features used for building models. Recently, a few data-driven techniques capable of handling complex image data have been proposed. In these techniques, as in traditional databases, iconic index structures are employed to store the image and shape representation in such a way that searching for a given shape or image feature can be conducted efficiently. Some of these techniques handle the insertion and deletion of shapes and/or image representations very efficiently and with very little influence on the overall system performance. However, the main disadvantage of all previous data-driven implementations is that they are main memory based. In the present paper, we describe a secondary memory implementation of data-driven indexed hypotheses along with some performance studies we have conducted.     

A knowledge model for unifying deductive and non-deductiveheterogeneous databases

A new, much-needed knowledge model for integrating heterogeneous knowledge represented by independently developed deductive rules and diverse data models has been developed and is presented. It has the strength and expressiveness necessary to unify seemingly disparate, autonomous components. Separate components, including rules, are modeled as functional units. A complete set of relationships, or links, between units based on domain interdependencies is defined, along with a set of integration operators. This approach resolves the unique and complex forms of conflict that arise from integrating deductive rules from autonomous sources, such as conflicts in meaning, redundancies and subsumption. This model has significant application to the creation of a common representation for future shared environments     

Intelligent and Pervasive Multimedia Systems

Recent advances in pervasive computing and the proliferation of multimedia-capable devices have stimulated the development of intelligent and pervasive multimedia applications. This special issue provides excellent coverage of this area, including interactive multimedia education, quality control and personalization of multimedia services, peer-to-peer multimedia streaming, mobile TV, and VoIP systems.