Visual structures for image browsing

Torres, Ricardo Silva; Silva, Celmar G.; Medeiros, Cláudia Bauzer; Rocha, Heloísa Vieira da

doi:10.1145/956873.956874

Cited by 24 publications

(21 citation statements)

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“…From the evidence we have here it is clear that users preferred the honeycomb layout -therefore the evidence we have (albeit limited) is that effective image seeking using a high density image interface is driven more by aesthetics. However there is a lot more work which needs to be done in this area, in particular the investigation of the many different layouts identified in the literature (Yoshizawa, 2004;Hearst, 2009;Torres;2003) and to provide more concrete evidence of the preference for density as against aesthetics.…”

Section: Discussionmentioning

confidence: 99%

“…A wide variety of different methods have been suggested for visualizing images (Yoshizawa, 2004;Hearst, 2009;Torres et al, 2003) including circles and other suggested by biological evolution such as the honeycomb. One particular method of displaying images on the screen is the sunflower pattern (figure 1), based on the ideas of Vogel (1979) and Rose (1999).…”

Section: Designing the High Density Image Interfacesmentioning

confidence: 99%

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Presenting and visualizing image results for professional image searchers: A field evaluation

Göker

Butterworth

MacFarlane

et al. 2017

Electronic Workshops in Computing

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Creative professionals such as journalists and copywriters are an important group of users who need to search for images as part of their work task. We describe the design and evaluation of a 'high density' image search interface targeted at this user group. We use a field evaluation together with a qualitative approach with creative professionals to gather feedback on the high density interface design. Our results show that creative professional viewed the interface favourably because they were able to get a quick overview of research results. We make suggestions for the appropriateness of high density interfaces for different work contexts, and further work in terms of layouts of images in high density interfaces. Image retrieval, user interface, results presentation, professional image searchers, high density UIs

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Section: Discussionmentioning

confidence: 99%

Section: Designing the High Density Image Interfacesmentioning

confidence: 99%

Presenting and visualizing image results for professional image searchers: A field evaluation

Göker

Butterworth

MacFarlane

et al. 2017

Electronic Workshops in Computing

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show abstract

“…There has been some work in providing more easy to use interactions for updating statistical models. For example, relevance feedback has been used for content-based image retrieval, where users are able to move images towards or away from a single image in order to portray pair-wise similarity or dissimilarity [72]. From there, an image retrieval algorithm determines the features and dimensions shared between the images that the user has determined as being similar.…”

Section: Synthesis Toolsmentioning

confidence: 99%

Semantic Interaction for Visual Analytics: Inferring Analytical Reasoning for Model Steering

Endert

2016

Synthesis Lectures on Visualization

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User interaction in visual analytic systems is critical to enabling visual data exploration.Through interacting with visualizations, users engage in sensemaking, a process of developing and understanding relationships within datasets through foraging and synthesis. For example, two-dimensional layouts of high-dimensional data can be generated by dimension reduction models, and provide users with an overview of the relationships between information. However, exploring such spatializations can require expertise with the internal mechanisms and parameters of these models.The core contribution of this work is semantic interaction, capable of steering such models without requiring expertise in dimension reduction models, but instead leveraging the domain expertise of the user. Semantic interaction infers the analytical reasoning of the user with model updates, steering the dimension reduction model for visual data exploration. As such, it is an approach to user interaction that leverages interactions

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“…10 leveraged the Pathfinder network scaling technique, originally developed for the analysis of proximity data in psychology, to organize a collection of images based on their color labels, texture, shape orientation etc. Torres et al 11 introduced a focus+context approach based on spiral and concentric rings for exploring query results in an image database. Yang et al 12 developed a scalable semantic image browser (SIB) based on the semantic content of images.…”

Section: Image Collection Organizationmentioning

confidence: 99%

Similarity-based visualization of large image collections

Wang

Reese

Zhang

et al. 2013

Information Visualization

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Effective techniques for organizing and visualizing large image collections are in growing demand as visual search gets increasingly popular. Targeting an online astronomy archive with thousands of images, we present our solution for image search and clustering based on the evaluation of image similarity using both visual and textual information. Time-consuming image similarity computation is accelerated using GPU. To lay out images, we introduce iMap, a treemap-based representation for visualizing and navigating image search and clustering results. iMap not only makes effective use of available display area to arrange images but also maintains stable update when images are inserted or removed during the query. We also develop an embedded visualization that integrates image tags for in-place search refinement. To show the effectiveness of our approach, we demonstrate experimental results, compare our iMap layout with a force-directed layout, and conduct a comparative user study. As a potential tool for astronomy education and outreach, we deploy our iMap to a large tiled display of nearly 50 million pixels.

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Visual structures for image browsing

Cited by 24 publications

References 0 publications

Presenting and visualizing image results for professional image searchers: A field evaluation

Presenting and visualizing image results for professional image searchers: A field evaluation

Semantic Interaction for Visual Analytics: Inferring Analytical Reasoning for Model Steering

Similarity-based visualization of large image collections

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