MultiFacet: A Faceted Interface for Browsing Large Multimedia Collections

Henry, Michael J.; Hampton, Shawn; Endert, Alex; Roberts, Ian; Payne, Deborah

doi:10.1109/ism.2013.66

Cited by 5 publications

(8 citation statements)

References 14 publications

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“…It combines analytic models such as feature extraction and classification with visual analytic approaches. MultiFacet is another example of visually analyzing multimedia data [HHE*13]. MultiFacet presents facets of each data type to users as interactive filters.…”

Section: Application Domainsmentioning

confidence: 99%

The State of the Art in Integrating Machine Learning into Visual Analytics

Endert

Ribarsky

Turkay

et al. 2017

Computer Graphics Forum

180

122

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Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-ofthe-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions.

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Section: Application Domainsmentioning

confidence: 99%

The State of the Art in Integrating Machine Learning into Visual Analytics

Endert

Ribarsky

Turkay

et al. 2017

Computer Graphics Forum

180

122

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show abstract

“…To allow a user-centric exploration of such a dataset, our system follows the data facet navigation principles [11], which are particularly well suited for such multi-dimensional data. In this context, a facet corresponds to the visualized dimension and the displayed set is the set of values attained by the data along the chosen dimension.…”

Section: Facet-based Recursive Explorationmentioning

confidence: 99%

Music archipelago, a facet-like music library comparison tool

Languénou

Kuntz

Greffard

2015

2015 IEEE 9th International Conference on Research Challenges in Information Science (RCIS)

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Stimulated by the rise of music recommendation services, the visualization of music collections has undergone a remarkable transformation over the last decade. In this communication, we focus on the visual comparison of personal music collections. We introduce an interactive and tridimensional system which allows their visualization using a music archipelago metaphor and relying on a facet-based engine. In the industrial context of music related services where social and sharing aspects are omnipresent, we also present a novel feature to enable the visual comparison of music collections.

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“…Facets are typically derived from pre-existing fields in the item's metadata or manually built from the analysis of the items. Using visual facets complementary to textual ones has already been addressed in a few previous works [2,1,9]. The principle is to automatically compute the facets by image clustering techniques and to display image links rather than textual links in the graphical user interface.…”

Section: Introductionmentioning

confidence: 99%

“…The principle is to automatically compute the facets by image clustering techniques and to display image links rather than textual links in the graphical user interface. As discussed in [2], visual faceted search might overcome several limitations of text oriented facets including incompleteness and inaccuracy of the meta-data. In this paper, we stress another interest of visual faceting in the case of expert data to be searched by any user.…”

Section: Introductionmentioning

confidence: 99%

“…The first one attempts to visually illustrate pre-existing expert facets in the data whereas the other one attempts to discover new facets from the visual contents. Both rely on the offline construction and clustering of a dense visual matching graph computed at the region level (contrary to previous visual faceted search proposals [2,1,9] that relied on image-centric clustering and global visual features).…”

Section: Introductionmentioning

confidence: 99%

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