Search (7 results, page 1 of 1)

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Abstract

The rapid growth of the numbers of images and their users as a result of the reduction in cost and increase in efficiency of the creation, storage, manipulation, and transmission of images poses challenges to those who organize and provide access to images. One of these challenges is similarity matching, a key component of current content-based image retrieval systems. Similarity matching often is implemented through similarity measures based on geometric models of similarity whose metric axioms are not satisfied by human similarity judgment data. This study is significant in that it is among the first known to test Tversky's contrast model, which equates the degree of similarity of two stimuli to a linear combination of their common and distinctive features, in the context of image representation and retrieval. Data were collected from 150 participants who performed an image description and a similarity judgment task. Structural equation modeling, correlation, and regression analyses confirmed the relationships between perceived features and similarity of objects hypothesized by Tversky. The results hold implications for future research that will attempt to further test the contrast model and assist designers of image organization and retrieval systems by pointing toward alternative document representations and similarity measures that more closely match human similarity judgments.

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Source

Paradigms and conceptual systems in knowledge organization: Proceedings of the Eleventh International ISKO Conference, 23-26 February 2010 Rome, Italy. Edited by Claudio Gnoli and Fulvio Mazzocchi

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Source

Paradigms and conceptual systems in knowledge organization: Proceedings of the Eleventh International ISKO conference, Rome, 23-26 February 2010, ed. Claudio Gnoli, Indeks, Frankfurt M

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Abstract

This paper presents an overview of the nature and the characteristics of the numerous problems encountered when a user tries to access a collection of images in a multilingual environment. The authors identify major research questions to be investigated to improve image retrieval effectiveness in a multilingual environment.

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Abstract

Faced with increasingly tight budgets many museums have come under pressure to explore new sources of income. The marketing of museum assets seems to offer one possibility especially since there is an apparently insatiable demand for content from the multimedia and 'edutainment' industries. Furthermore, providing access to the cultural heritage of Europe has come to be seen as a major aim of the EU and various programmes have been set up to encourage and facilitate this. Apart from the economic potential of digital museum content, the concomitant legal and security issues are investigated.

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Abstract

There are currently two dominant interface types for searching and browsing large image collections: keywordbased search, and searching by overall similarity to sample images. We present an alternative based on enabling users to navigate along conceptual dimensions that describe the images. The interface makes use of hierarchical faceted metadata and dynamically generated query previews. A usability study, in which 32 art history students explored a collection of 35,000 fine arts images, compares this approach to a standard image search interface. Despite the unfamiliarity and power of the interface (attributes that often lead to rejection of new search interfaces), the study results show that 90% of the participants preferred the metadata approach overall, 97% said that it helped them learn more about the collection, 75% found it more flexible, and 72% found it easier to use than a standard baseline system. These results indicate that a category-based approach is a successful way to provide access to image collections.

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Date

6. 1.2011 11:22:12