Proceedings: GI 2014

Fast forward with your VCR: visualizing single-video viewing statistics for navigation and sharing

Abir Al-Hajri , Matthew Fong , Gregor Miller , Sidney Fels

Proceedings of Graphics Interface 2014: Montréal, Québec, Canada, 7 - 9 May 2014, 123-128

DOI 10.20380/GI2014.16

  • Bibtex

    @inproceedings{Al-Hajri:2014:10.20380/GI2014.16,
    author = {Al-Hajri, Abir and Fong, Matthew and Miller, Gregor and Fels, Sidney},
    title = {Fast forward with your VCR: visualizing single-video viewing statistics for navigation and sharing},
    booktitle = {Proceedings of Graphics Interface 2014},
    series = {GI 2014},
    year = {2014},
    issn = {0713-5424},
    isbn = {978-1-4822-6003-8},
    location = {Montr{\'e}al, Qu{\'e}bec, Canada},
    pages = {123--128},
    numpages = {6},
    doi = {10.20380/GI2014.16},
    publisher = {Canadian Human-Computer Communications Society},
    address = {Toronto, Ontario, Canada},
    }

Abstract

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.