TY - GEN
T1 - Fast forward with your VCR
T2 - 40th Graphics Interface Conference, GI 2014
AU - Al-Hajri, Abir
AU - Fong, Matthew
AU - Miller, Gregor
AU - Fels, Sidney
PY - 2014
Y1 - 2014
N2 - Online video viewing has seen explosive growth, yet simple tools to facilitate navigation and sharing of the large video space have not kept pace. We propose the use of single-video viewing statistics as the basis for a visualization of video called the View Count Record (VCR). Our novel visualization utilizes variable-sized thumbnails to represent the popularity (or affectiveness) of video intervals, and provides simple mechanisms for fast navigation, informed search, video previews, simple sharing and summarization. The viewing statistics are generated from an individual's video consumption, or crowd-sourced from many people watching the same video; both provide different scenarios for application (e.g. implicit tagging of interesting events for an individual, and quickly navigating to others' most-viewed scenes for crowd-sourced). A comparative user study evaluates the effectiveness of the VCR by asking participants to share previously-seen affective parts within videos. Experimental results demonstrate that the VCR outperforms the state-of-the-art in a search task, and has been welcomed as a recommendation tool for clips within videos (using crowd-sourced statistics). It is perceived by participants as effective, intuitive and strongly preferred to current methods. Copyright held by authors.
AB - Online video viewing has seen explosive growth, yet simple tools to facilitate navigation and sharing of the large video space have not kept pace. We propose the use of single-video viewing statistics as the basis for a visualization of video called the View Count Record (VCR). Our novel visualization utilizes variable-sized thumbnails to represent the popularity (or affectiveness) of video intervals, and provides simple mechanisms for fast navigation, informed search, video previews, simple sharing and summarization. The viewing statistics are generated from an individual's video consumption, or crowd-sourced from many people watching the same video; both provide different scenarios for application (e.g. implicit tagging of interesting events for an individual, and quickly navigating to others' most-viewed scenes for crowd-sourced). A comparative user study evaluates the effectiveness of the VCR by asking participants to share previously-seen affective parts within videos. Experimental results demonstrate that the VCR outperforms the state-of-the-art in a search task, and has been welcomed as a recommendation tool for clips within videos (using crowd-sourced statistics). It is perceived by participants as effective, intuitive and strongly preferred to current methods. Copyright held by authors.
KW - H.1.2. [models and principles]: user/machine systems
KW - H.5.2. [information interfaces and presentation]: user interfaces
UR - http://www.scopus.com/inward/record.url?scp=84904805629&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904805629&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84904805629
SN - 9781482260038
T3 - Proceedings - Graphics Interface
SP - 123
EP - 128
BT - Proceedings - Graphics Interface 2014, GI 2014
PB - Canadian Information Processing Society
Y2 - 7 May 2014 through 9 May 2014
ER -