MediaTable: Interactive Categorization of Multimedia Collections

Rooij, O. de; Wijk, Jarke J. van; Worring, Marcel

doi:10.1109/mcg.2010.66

Cited by 32 publications

(30 citation statements)

References 12 publications

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“…The first browser based on the spreadsheet paradigm is PhotoSpread by Kandel et al [39] MediaTable by de Rooij et al visualizes each item in one row, with its content, annotations, and metadata forming individual columns [20]. Multimedia Pivot Tables by Worring and Koelma allow the analyst to define the rows and columns of the table herself [90].…”

Section: Multimedia Visualizationmentioning

confidence: 99%

Towards interactive, intelligent, and integrated multimedia analytics

Zahálka

Worring

2014

2014 IEEE Conference on Visual Analytics Science and Technology (VAST)

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Abstract-The size and importance of visual multimedia collections grew rapidly over the last years, creating a need for sophisticated multimedia analytics systems enabling large-scale, interactive, and insightful analysis. These systems need to integrate the human's natural expertise in analyzing multimedia with the machine's ability to process large-scale data. The paper starts off with a comprehensive overview of representation, learning, and interaction techniques from both the human's and the machine's point of view. To this end, hundreds of references from the related disciplines (visual analytics, information visualization, computer vision, multimedia information retrieval) have been surveyed. Based on the survey, a novel general multimedia analytics model is synthesized. In the model, the need for semantic navigation of the collection is emphasized and multimedia analytics tasks are placed on the explorationsearch axis. The axis is composed of both exploration and search in a certain proportion which changes as the analyst progresses towards insight. Categorization is proposed as a suitable umbrella task realizing the exploration-search axis in the model. Finally, the pragmatic gap, defined as the difference between the tight machine categorization model and the flexible human categorization model is identified as a crucial multimedia analytics topic.Index Terms-Multimedia (image/video/music) visualization, machine learning.

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Section: Multimedia Visualizationmentioning

confidence: 99%

Towards interactive, intelligent, and integrated multimedia analytics

Zahálka

Worring

2014

2014 IEEE Conference on Visual Analytics Science and Technology (VAST)

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“…Hierarchical visualizations for understanding news data, based on analysis of closed-captions, are presented in [27] using hyperbolic trees as visualization to explore the hierarchical structure of the data. Concept detectors are used in both [46] and [9]. Where [46] uses grids and similarity based displays, [9] uses a heatmap like visualization simultaneously showing a large set of ranked lists.…”

Section: A Multimedia Visualization and Analyticsmentioning

confidence: 99%

“…Concept detectors are used in both [46] and [9]. Where [46] uses grids and similarity based displays, [9] uses a heatmap like visualization simultaneously showing a large set of ranked lists. The above methods are all essentially search-focused.…”

Section: A Multimedia Visualization and Analyticsmentioning

confidence: 99%

“…All insight is derived from the members assigned to the different analytic categories. To emphasize the action of adding items to categories we denote the categories as buckets [9], a metaphor for a place where elements can be put, each having a color consistently used throughout the system. A bucket B is thus defined by:…”

Section: B Multimedia Insightsmentioning

confidence: 99%

“…Some interesting early approaches in this direction, solely relying on the metadata for querying and visualizing the image collection, have been proposed in [5] [20]. The MediaTable [9] adds automatic concept detection to analyze and describe the visual content and organizes the results in the tabular form akin to what is used in spreadsheets. Arguably one of the most powerful features of spreadsheets for understanding complex data is the pivot table.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multimedia Pivot Tables for Multimedia Analytics on Image Collections

Worring

Koelma

Zahálka

2016

IEEE Trans. Multimedia

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Abstract-We propose a multimedia analytics solution for getting insight in image collections by extending the powerful analytic capabilities of pivot tables, found in the ubiquitous spreadsheets, to multimedia. We formalize the concept of multimedia pivot tables and give design rules and methods for the multimodal summarization, structuring, and browsing of the collection based on these tables, all optimized to support an analyst in getting structural and conclusive insights. Our proposed solution provides truly interactive analytics on the visual content of image collections through concept detection results, as well as tags, geolocation, time and other metadata. We have performed user experiments with novice users on a dataset from Flickr to improve the initial design and with expert users in marketing and multimedia analysis on two domain specific datasets collected from Instagram. The results show that analysts are indeed capable of deriving structural and conclusive insights using the proposed multimedia analytics solution. On our website videos of the system in action are available.

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Multimedia Analytics for Image Collection Forensics

Worring

2015

Handbook of Digital Forensics of Multimedia Data and Devices

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MediaTable: Interactive Categorization of Multimedia Collections

Cited by 32 publications

References 12 publications

Towards interactive, intelligent, and integrated multimedia analytics

Towards interactive, intelligent, and integrated multimedia analytics

Multimedia Pivot Tables for Multimedia Analytics on Image Collections

Multimedia Analytics for Image Collection Forensics

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