114,948 research outputs found

    An adaptive approach for image organisation and retrieval

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    We propose and evaluate an adaptive approach towards content-based image retrieval (CBIR), which is based on the Ostensive Model of developing information needs. We use ostensive relevance to capture the user's current interest and tailor the retrieval accordingly. Our approach supports content-assisted browsing, by incorporating an adaptive query learning scheme based on implicit feedback from the user. Textual and colour features are employed to characterise images. Evidence from these features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, task-oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. Its strengths are considered to lie in its ability to adapt to the user's need, and its very intuitive and fluid way of operation

    An adaptive technique for content-based image retrieval

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    We discuss an adaptive approach towards Content-Based Image Retrieval. It is based on the Ostensive Model of developing information needs—a special kind of relevance feedback model that learns from implicit user feedback and adds a temporal notion to relevance. The ostensive approach supports content-assisted browsing through visualising the interaction by adding user-selected images to a browsing path, which ends with a set of system recommendations. The suggestions are based on an adaptive query learning scheme, in which the query is learnt from previously selected images. Our approach is an adaptation of the original Ostensive Model based on textual features only, to include content-based features to characterise images. In the proposed scheme textual and colour features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, work-task oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. This is due to its ability to adapt to the user's need, its intuitiveness and the fluid way in which it operates. Studying and comparing the nature of the underlying information need, it emerges that our approach elicits changes in the user's need based on the interaction, and is successful in adapting the retrieval to match the changes. In addition, a preliminary study of the retrieval performance of the ostensive relevance feedback scheme shows that it can outperform a standard relevance feedback strategy in terms of image recall in category search

    The Simplest Evaluation Measures for XML Information Retrieval that Could Possibly Work

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    This paper reviews several evaluation measures developed for evaluating XML information retrieval (IR) systems. We argue that these measures, some of which are currently in use by the INitiative for the Evaluation of XML Retrieval (INEX), are complicated, hard to understand, and hard to explain to users of XML IR systems. To show the value of keeping things simple, we report alternative evaluation results of official evaluation runs submitted to INEX 2004 using simple metrics, and show its value for INEX

    Web document summarisation: a task-oriented evaluation

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    We present a query-biased summarisation interface for Web searching. The summarisation system has been specifically developed to act as a component in existing Web search interfaces. The summaries allow the user to more effectively assess the content of Web pages. We also present an experimental investigation of this approach. Our experimental results shows the system appears to be more useful and effective in helping users gauge document relevance than the traditional ranked titles/abstracts approach

    An Investigation on Text-Based Cross-Language Picture Retrieval Effectiveness through the Analysis of User Queries

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    Purpose: This paper describes a study of the queries generated from a user experiment for cross-language information retrieval (CLIR) from a historic image archive. Italian speaking users generated 618 queries for a set of known-item search tasks. The queries generated by user’s interaction with the system have been analysed and the results used to suggest recommendations for the future development of cross-language retrieval systems for digital image libraries. Methodology: A controlled lab-based user study was carried out using a prototype Italian-English image retrieval system. Participants were asked to carry out searches for 16 images provided to them, a known-item search task. User’s interactions with the system were recorded and queries were analysed manually quantitatively and qualitatively. Findings: Results highlight the diversity in requests for similar visual content and the weaknesses of Machine Translation for query translation. Through the manual translation of queries we show the benefits of using high-quality translation resources. The results show the individual characteristics of user’s whilst performing known-item searches and the overlap obtained between query terms and structured image captions, highlighting the use of user’s search terms for objects within the foreground of an image. Limitations and Implications: This research looks in-depth into one case of interaction and one image repository. Despite this limitation, the discussed results are likely to be valid across other languages and image repository. Value: The growing quantity of digital visual material in digital libraries offers the potential to apply techniques from CLIR to provide cross-language information access services. However, to develop effective systems requires studying user’s search behaviours, particularly in digital image libraries. The value of this paper is in the provision of empirical evidence to support recommendations for effective cross-language image retrieval system design.</p
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