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Poster of technical demo presented at ACM Multimedia 2012
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LikeLines: Collecting Timecode-level Feedback
for Web Videos through User Interactions
Raynor Vliegendhart, Martha Larson, and Alan Hanjalic
20th ACM international conference on Multimedia, Nara, Japan, 2012
Future Work
● For what kinds of video is timecode-level feedback useful?
● How should user interactions be interpreted?
● How to fuse timecode-level feedback with content analysis
without encouraging snowball effects?
● Can timecode-level data be linked to queries to recommend
relevant jump points?
● How to collect a critical mass of timecode-level data by
incentivizing users to interact with the system?
Contact: [email protected]
Implementation
● Video player component implemented in JavaScript and HTML5.
● Out-of-the-box support for YouTube and HTML5 videos.
● Video player component communicates with a back-end server
using JSON(P).
● Back-end server reference implementation is written in Python.
Multimedia Information Retrieval Lab, Delft University of Technology
@ShinNoNoir
Problem
● Problem: Providing users with a navigable heat map of interesting
regions of the video they are watching.
● Motivation: Conventional time sliders do not make the inner structure
of the video apparent, making it hard to navigate to the interesting bits.
Approach
A Web video player component with a navigable heat map, that:
● Uses multimedia content analysis to seed the heat map.
● Captures implicit and explicit user feedback at the timecode-level
to refine the heat map.
System Overview
● Video player component, augmented with:
● Navigable heat map that allows users to jump directly to “hot”
areas;
● Time-sensitive “like” button that allows users to explicitly like
particular points in the video.
● Captures user interactions:
● Implicit feedback such as playing, pausing and seeking;
● Explicit “likes” expressed by the user.
● Combines content analysis and captured user interactions to compute
a video’s heat map.
● Back-end interaction session server stores and aggregates per video:
● All interaction sessions between each user and player;
● Initial multimedia content analysis of the video.
Source code:
https://github.com/delftmir/likelines-player
viewers
content analysis
LikeLines player
play
pause
seek
like
... interaction
session server
content analysis
t (s)
user feedback 1
t (s)
user feedback n
t (s) + + + …
<script type="text/javascript">
var player = new LikeLines.Player('playerDiv', {
video: 'http://www.youtube.com/watch?v=wPTilA0XxYE',
backend: 'http://backend:9090/'
});
</script>
