Improving Interactive Known-Item Search in Video with the Keyframe Navigation Tree

Hudelist, Marco A.; Schoeffmann, Klaus; Xu, Qing

doi:10.1007/978-3-319-14445-0_27

Cited by 8 publications

(10 citation statements)

References 25 publications

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“…The state of the art review has revealed that while current approaches do provide interesting applications, they are limited in utilizing the potential of video while representing the content. For example hypervideos and interactive video navigation systems do allow users to explore video content in a nonlinear and interactive manner and there have been some attempts to allow users to explore content at different levels of detail [36,37,57,58]. Multimodality of video content is still underutilized in the presentation of content to the user.…”

Section: Gaps In the State Of The Artmentioning

confidence: 99%

Automatic Transformation of a Video Using Multimodal Information for an Engaging Exploration Experience

et al. 2020

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Exploring the content of a video is typically inefficient due to the linear streamed nature of its media and the lack of interactivity. While different approaches have been proposed for enhancing the exploration experience of video content, the general view of video content has remained basically the same, that is, a continuous stream of images. It is our contention that such a conservative view on video limits its potential value as a content source. This paper presents An Alternative Representation of Video via feature Extraction (RAAVE), a novel approach to transform videos from a linear stream of content into an adaptive interactive multimedia document and thereby enhance the exploration potential of video content by providing a more engaging user experience. We explore the idea of viewing video as a diverse multimedia content source, opening new opportunities and applications to explore and consume video content. A modular framework and algorithm for the representation engine and template collection is described. The representation engine based approach is evaluated through development of a prototype system grounded on the design of the proposed approach, allowing users to perform multiple content exploration tasks within a video. The evaluation demonstrated RAAVE’s ability to provide users with a more engaging, efficient and effective experience than a typical multimedia player while performing video exploration tasks.

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Section: Gaps In the State Of The Artmentioning

confidence: 99%

Automatic Transformation of a Video Using Multimodal Information for an Engaging Exploration Experience

et al. 2020

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“…We introduce an approach that is inspired by two successful systems from last year's edition of the Video Search Showcase (VSS) [16,17] as well as our earlier work on mobile video browsers [8,9]. Blaẑek et al showed a system based on signature-based similarity models that won the contest.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Collaborative Video Search Combining Video Retrieval with Human-Based Visual Inspection

Hudelist

Cobârzan

Beecks

et al. 2016

MultiMedia Modeling

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We propose a novel video browsing approach that aims at optimally integrating traditional, machine-based retrieval methods with an interface design optimized for human browsing performance. Advanced video retrieval and filtering (e.g., via color and motion signatures, and visual concepts) on a desktop is combined with a storyboardbased interface design on a tablet optimized for quick, brute-force visual inspection. Both modules run independently but exchange information to significantly minimize the data for visual inspection and compensate mistakes made by the search algorithms.

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“…Multi-modal input for web searching has been researched to improve on effectiveness and efficiency (Goel, Giangreco, Rossetto, Tănase, & Schuldt, 2017;Pinho, Godinho, Valente, & Costa, 2017;Xu et al, 2015). As the efficiency and effectiveness of multi-modal retrieval improves, there is potential for an interface design to better integrate the multi-modal input.…”

Section: Discussion With Information Retrieval Expertsmentioning

confidence: 99%

“…In developing the potential video retrieval interface and features to support exploratory searches, it is important to review the developments related to the information retrieval domain. Due to the multiple modalities of videos, aural and visual cues were used for indexing and interface strategies for video retrieval (Xu et al, 2015). As a result, video retrieval interfaces may be more complex when compared to a retrieval interface for textual resources.…”

Section: Developments In Interface and Featuresmentioning

confidence: 99%

“…The hierarchical visualization of keyframes in a video was used to represent the content of a video and to provide in-video navigation (Hudelist, Schoeffmann, & Xu, 2015). The researchers found that this navigation approach supported faster known-item search within the video as compared to a common video player and the users had lower mental load while doing so.…”

Section: Resource Consumptionmentioning

confidence: 99%

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Design and evaluation of a video retrieval environment for exploratory searches in a learning context

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Improving Interactive Known-Item Search in Video with the Keyframe Navigation Tree

Cited by 8 publications

References 25 publications

Automatic Transformation of a Video Using Multimodal Information for an Engaging Exploration Experience

Automatic Transformation of a Video Using Multimodal Information for an Engaging Exploration Experience

Collaborative Video Search Combining Video Retrieval with Human-Based Visual Inspection

Design and evaluation of a video retrieval environment for exploratory searches in a learning context

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