Evaluating tag-based information access in image collections

Trattner, Christoph; Lin, Yiling; Parra, Denis; Yue, Zhen; Real, William A.; Brusilovsky, Peter

doi:10.1145/2309996.2310016

Cited by 23 publications

(12 citation statements)

References 37 publications

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“…Examples of this tagging approach are Delicious®, Pinboard® (favorite websites), Youtube® (video), Flickr® (photo's), Connotea® (science) or Last.fm® (music). Few such studies exist (Trattner, Lin, Parra, et al, 2012) investigating how to construct and visualize tag clouds. The same is true for comparing the use of tag clouds and more traditional hierarchical menus (Civan, Jones, Klasnja & Bruce, 2009;Voit, Andrews & Slany, 2012).…”

Section: Navigational Support: Hierarchical or Tag-cloudmentioning

confidence: 99%

Learning and navigating in hypertext: Navigational support by hierarchical menu or tag cloud?

Walhout

Brand‐Gruwel

Jarodzka

et al. 2015

Computers in Human Behavior

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As hypertext learning environments (HLE) are widely used in education, it is important to study and know the effects and consequences of its use. HLEs are non-linear which means that students have to develop ways of navigating through them. Thus, developing interfaces that facilitate and even guide navigation is important for learning. Research showed that successful learning in HLEs depends on both learner characteristics and HLE features. This study investigated an HLE navigation feature (navigational support with either a tag-cloud or conventional hierarchical menu), task complexity (fact-finding vs. information-gathering task) and a user characteristic (gender). Results show that neither navigational support nor gender is associated with differences in task performance. However, there are differences in information processing. Participants using a tag clouds looked longer at the navigational support and shorter at the overview pages. Combined with fewer revisits of webpages in the tag cloud condition, this indicates a more focused selection of pages. The deeper processing of information needed for the information-gathering task was reflected in fewer visits to, but longer viewing times of pages. As no differences in task performance were found, tag clouds seem to be as effective for performance as more traditional navigation structures for navigational support.

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Section: Navigational Support: Hierarchical or Tag-cloudmentioning

confidence: 99%

Learning and navigating in hypertext: Navigational support by hierarchical menu or tag cloud?

Walhout

Brand‐Gruwel

Jarodzka

et al. 2015

Computers in Human Behavior

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“…It might also be useful to students as they learn how to analyze and describe images. Whether an interface requiring this much mental discipline would be accepted by users in the broader world of social tagging is an open question (see Trattner et al [2012] for one small study that includes a faceted image search interface).…”

Section: Support the Design Of Improved Interfaces For Image Tagging mentioning

confidence: 99%

Modeling and analyzing the topicality of art images

Huang

Soergel

Klavans

2015

Asso for Info Science & Tech

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This study demonstrates an improved conceptual foundation to support well‐structured analysis of image topicality. First we present a conceptual framework for analyzing image topicality, explicating the layers, the perspectives, and the topical relevance relationships involved in modeling the topicality of art images. We adapt a generic relevance typology to image analysis by extending it with definitions and relationships specific to the visual art domain and integrating it with schemes of image‐text relationships that are important for image subject indexing. We then apply the adapted typology to analyze the topical relevance relationships between 11 art images and 768 image tags assigned by art historians and librarians. The original contribution of our work is the topical structure analysis of image tags that allows the viewer to more easily grasp the content, context, and meaning of an image and quickly tune into aspects of interest; it could also guide both the indexer and the searcher to specify image tags/descriptors in a more systematic and precise manner and thus improve the match between the two parties. An additional contribution is systematically examining and integrating the variety of image‐text relationships from a relevance perspective. The paper concludes with implications for relational indexing and social tagging.

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“…Tag clouds are seen as a "social" way to visualize information [10], and much work has been driven in this direction [4]. In the context of image retrieval, Callegari and Morreale [2] showed that less frequently used words in a tag cloud can significantly increase retrieval speed for the images associated with the tags.…”

Section: Related Workmentioning

confidence: 99%

“…In this regard, tag clouds have been shown to be a useful approach [2,4,10]. For instance, Flickr features "tag clusters"…”

Section: Introductionmentioning

confidence: 99%

Relevant Clouds: Leveraging Relevance Feedback to Build Tag Clouds for Image Search

Leiva

Villegas

Paredes

2013

Lecture Notes in Computer Science

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Abstract. Previous work in the literature has been aimed at exploring tag clouds to improve image search and potentially increase retrieval performance. However, to date none has considered the idea of building tag clouds derived from relevance feedback. We propose a simple approach to such an idea, where the tag cloud gives more importance to the words from the relevant images than the non-relevant ones. A preliminary study with 164 queries inspected by 14 participants over a 30M dataset of automatically annotated images showed that 1) tag clouds derived this way are found to be informative: users considered roughly 20% of the presented tags to be relevant for any query at any time; and 2) the importance given to the tags correlates with user judgments: tags ranked in the first positions tended to be perceived more often as relevant to the topic that users had in mind.

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Evaluating tag-based information access in image collections

Cited by 23 publications

References 37 publications

Learning and navigating in hypertext: Navigational support by hierarchical menu or tag cloud?

Learning and navigating in hypertext: Navigational support by hierarchical menu or tag cloud?

Modeling and analyzing the topicality of art images

Relevant Clouds: Leveraging Relevance Feedback to Build Tag Clouds for Image Search

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