Image retrieval by hypertext links

This paper presents a model for retrieval of images from a large World Wide Web based collection. Rather than considering complex visual recognition algorithms, the model presented is based on combining evidence of the text content and hypertext structure of the Web. The paper shows that certain types of query are amply served by this form of representation. It also presents a novel means of gathering relevance judgements

Formal models, usability and related work in IR (editorial for special edition)

The Glasgow IR group has carried out both theoretical and empirical work, aimed at giving end users efficient and effective access to large collections of multimedia data

Development and evaluation of clustering techniques for finding people

Typically in a large organisation much expertise and knowledge is held informally within employees' own memories. When employees leave an organisation many documented links that go through that person are broken and no mechanism is usually available to overcome these broken links. This match making problem is related to the problem of finding potential work partners in a large and distributed organisation. This paper reports a comparative investigation into using standard information retrieval techniques to group employees together based on their webpages. This information can, hopefully, be subsequently used to redirect broken links to people who worked closely with a departed employee or used to highlight people, say indifferent departments, who work on similar topics. The paper reports the design and positive results of an experiment conducted at Risø National Laboratory comparing four different IR searching and clustering approaches using real users' web pages

User experiments with the Eurovision cross-language image retrieval system

In this paper we present Eurovision, a text-based system for cross-language (CL) image retrieval. The system is evaluated by multilingual users for two search tasks with the system configured in English and five other languages. To our knowledge this is the first published set of user experiments for CL image retrieval. We show that: (1) it is possible to create a usable multilingual search engine using little knowledge of any language other than English, (2) categorizing images assists the user's search, and (3) there are differences in the way users search between the proposed search tasks. Based on the two search tasks and user feedback, we describe important aspects of any CL image retrieval system

Improving patient record search: A meta-data based approach

The International Classification of Diseases (ICD) is a type of meta-data found in many Electronic Patient Records. Research to explore the utility of these codes in medical Information Retrieval (IR) applications is new, and many areas of investigation remain, including the question of how reliable the assignment of the codes has been. This paper proposes two uses of the ICD codes in two different contexts of search: Pseudo-Relevance Judgments (PRJ) and Pseudo-Relevance Feedback (PRF). We find that our approach to evaluate the TREC challenge runs using simulated relevance judgments has a positive correlation with the TREC official results, and our proposed technique for performing PRF based on the ICD codes significantly outperforms a traditional PRF approach. The results are found to be consistent over the two years of queries from the TREC medical test collection

Text-based approaches for non-topical image categorization

The rapid expansion of multimedia digital collections brings to the fore the need for classifying not only text documents but their embedded non-textual parts as well. We propose a model for basing classification of multimedia on broad, non-topical features, and show how information on targeted nearby pieces of text can be used to effectively classify photographs on a first such feature, distinguishing between indoor and outdoor images. We examine several variations to a TF*IDF-based approach for this task, empirically analyze their effects, and evaluate our system on a large collection of images from current news newsgroups. In addition, we investigate alternative classification and evaluation methods, and the effects that secondary features have on indoor/outdoor classification. Using density estimation over the raw TF*IDF values, we obtain a classification accuracy of 82%, a number that outperforms baseline estimates and earlier, image-based approaches, at least in the domain of news articles, and that nears the accuracy of humans who perform the same task with access to comparable information

Extracting Semantic Information from Visual Data: A Survey

The traditional environment maps built by mobile robots include both metric ones and topological ones. These maps are navigation-oriented and not adequate for service robots to interact with or serve human users who normally rely on the conceptual knowledge or semantic contents of the environment. Therefore, the construction of semantic maps becomes necessary for building an effective human-robot interface for service robots. This paper reviews recent research and development in the field of visual-based semantic mapping. The main focus is placed on how to extract semantic information from visual data in terms of feature extraction, object/place recognition and semantic representation methods