Description of an experimental on-line, minicomputer-based information retrieval system /

Thesis (M. S.)--University of Illinois at Urbana-Champaign.

Information retrieval and machine translation : based on the International Conference for Standards on a Common Language for Machine Searching and Translation /

Mode of access: Internet.

Automatic texture segmentation for texture-based image retrieval

Texture-segmentation is the crucial initial step for texture-based image retrieval. Texture is the main difficulty faced to a segmentation method. Many image segmentation algorithms either can’t handle texture properly or can’t obtain texture features directly during segmentation which can be used for retrieval purpose. This paper describes an automatic texture segmentation algorithm based on a set of features derived from wavelet domain, which are effective in texture description for retrieval purpose. Simulation results show that the proposed algorithm can efficiently capture the textured regions in arbitrary images, with the features of each region extracted as well. The features of each textured region can be directly used to index image database with applications as texture-based image retrieval.

Towards more personalized web: Extraction and integration of dynamic content from the Web

Information and content integration are believed to be a possible solution to the problem of information overload in the Internet. The article is an overview of a simple solution for integration of information and content on the Web. Previous approaches to content extraction and integration are discussed, followed by introduction of a novel technology to deal with the problems, based on XML processing. The article includes lessons learned from solving issues of changing webpage layout, incompatibility with HTML standards and multiplicity of the results returned. The method adopting relative XPath queries over DOM tree proves to be more robust than previous approaches to Web information integration. Furthermore, the prototype implementation demonstrates the simplicity that enables non-professional users to easily adopt this approach in their day-to-day information management routines.

Concept-based document readability in domain specific information retrieval

Domain specific information retrieval has become in demand. Not only domain experts, but also average non-expert users are interested in searching domain specific (e.g., medical and health) information from online resources. However, a typical problem to average users is that the search results are always a mixture of documents with different levels of readability. Non-expert users may want to see documents with higher readability on the top of the list. Consequently the search results need to be re-ranked in a descending order of readability. It is often not practical for domain experts to manually label the readability of documents for large databases. Computational models of readability needs to be investigated. However, traditional readability formulas are designed for general purpose text and insufficient to deal with technical materials for domain specific information retrieval. More advanced algorithms such as textual coherence model are computationally expensive for re-ranking a large number of retrieved documents. In this paper, we propose an effective and computationally tractable concept-based model of text readability. In addition to textual genres of a document, our model also takes into account domain specific knowledge, i.e., how the domain-specific concepts contained in the document affect the document’s readability. Three major readability formulas are proposed and applied to health and medical information retrieval. Experimental results show that our proposed readability formulas lead to remarkable improvements in terms of correlation with users’ readability ratings over four traditional readability measures.

Structured ontology and information retrieval for email search and discovery

This paper discusses an document discovery tool based on formal concept analysis. The program allows users to navigate email using a visual lattice metaphor rather than a tree. It implements a virtual file structure over email where files and entire directories can appear in multiple positions. The content and shape of the lattice formed by the conceptual ontology can assist in email discovery. The system described provides more flexibility in retrieving stored emails than what is normally available in email clients. The paper discusses how conceptual ontologies can leverage traditional document retrieval systems.

Neural network-based wind vector retrieval from satellite scatterometer data

Obtaining wind vectors over the ocean is important for weather forecasting and ocean modelling. Several satellite systems used operationally by meteorological agencies utilise scatterometers to infer wind vectors over the oceans. In this paper we present the results of using novel neural network based techniques to estimate wind vectors from such data. The problem is partitioned into estimating wind speed and wind direction. Wind speed is modelled using a multi-layer perceptron (MLP) and a sum of squares error function. Wind direction is a periodic variable and a multi-valued function for a given set of inputs; a conventional MLP fails at this task, and so we model the full periodic probability density of direction conditioned on the satellite derived inputs using a Mixture Density Network (MDN) with periodic kernel functions. A committee of the resulting MDNs is shown to improve the results.

Neural network-based wind vector retrieval from satellite scatterometer data

Obtaining wind vectors over the ocean is important for weather forecasting and ocean modelling. Several satellite systems used operationally by meteorological agencies utilise scatterometers to infer wind vectors over the oceans. In this paper we present the results of using novel neural network based techniques to estimate wind vectors from such data. The problem is partitioned into estimating wind speed and wind direction. Wind speed is modelled using a multi-layer perceptron (MLP) and a sum of squares error function. Wind direction is a periodic variable and a multi-valued function for a given set of inputs; a conventional MLP fails at this task, and so we model the full periodic probability density of direction conditioned on the satellite derived inputs using a Mixture Density Network (MDN) with periodic kernel functions. A committee of the resulting MDNs is shown to improve the results.

Document retrieval based on a cognitive model of dialogue

Owing to the rise in the volume of literature, problems arise in the retrieval of required information. Various retrieval strategies have been proposed, but most of that are not flexible enough for their users. Specifically, most of these systems assume that users know exactly what they are looking for before approaching the system, and that users are able to precisely express their information needs according to l aid- down specifications. There has, however, been described a retrieval program THOMAS which aims at satisfying incompletely- defined user needs through a man- machine dialogue which does not require any rigid queries. Unlike most systems, Thomas attempts to satisfy the user's needs from a model which it builds of the user's area of interest. This model is a subset of the program's "world model" - a database in the form of a network where the nodes represent concepts since various concepts have various degrees of similarities and associations, this thesis contends that instead of models which assume equal levels of similarities between concepts, the links between the concepts should have values assigned to them to indicate the degree of similarity between the concepts. Furthermore, the world model of the system should be structured such that concepts which are related to one another be clustered together, so that a user- interaction would involve only the relevant clusters rather than the entire database such clusters being determined by the system, not the user. This thesis also attempts to link the design work with the current notion in psychology centred on the use of the computer to simulate human cognitive processes. In this case, an attempt has been made to model a dialogue between two people - the information seeker and the information expert. The system, called Thomas-II, has been implemented and found to require less effort from the user than Thomas.

Task-based annotation and retrieval for image information management

Continuing advances in digital image capture and storage are resulting in a proliferation of imagery and associated problems of information overload in image domains. In this work we present a framework that supports image management using an interactive approach that captures and reuses task-based contextual information. Our framework models the relationship between images and domain tasks they support by monitoring the interactive manipulation and annotation of task-relevant imagery. During image analysis, interactions are captured and a task context is dynamically constructed so that human expertise, proficiency and knowledge can be leveraged to support other users in carrying out similar domain tasks using case-based reasoning techniques. In this article we present our framework for capturing task context and describe how we have implemented the framework as two image retrieval applications in the geo-spatial and medical domains. We present an evaluation that tests the efficiency of our algorithms for retrieving image context information and the effectiveness of the framework for carrying out goal-directed image tasks. © 2010 Springer Science+Business Media, LLC.

Task-based image annotation and retrieval

In order to address problems of information overload in digital imagery task domains we have developed an interactive approach to the capture and reuse of image context information. Our framework models different aspects of the relationship between images and domain tasks they support by monitoring the interactive manipulation and annotation of task-relevant imagery. The approach allows us to gauge a measure of a user's intentions as they complete goal-directed image tasks. As users analyze retrieved imagery their interactions are captured and an expert task context is dynamically constructed. This human expertise, proficiency, and knowledge can then be leveraged to support other users in carrying out similar domain tasks. We have applied our techniques to two multimedia retrieval applications for two different image domains, namely the geo-spatial and medical imagery domains. © Springer-Verlag Berlin Heidelberg 2007.