Paper Info
Title
ViZig: Anchor Points based Non-Linear Navigation and Summarization in Educational Videos.
Abstract
Instructional videos are one of the most popular ways of teaching and learning in an online setting. However, navigation in videos is linear as compared to other instructional resources such as textbooks, where a table of topics and a multi-faceted index of different anchor points i.e., list of figures, tables aid in efficiently navigating to a desired point of interest. There is a lack of appropriate techniques and interfaces which can support such textbook-style navigation in instructional videos. This paper presents a novel approach to automatically localize and classify different anchor points in a video including figures, tables, equations, flowcharts, code snippets and charts. Our approach uses a deep convolution neural network in a semi-supervised fashion where the training data is obtained from the unconstrained Internet images. On an anchor point dataset of about 10K images, the proposed algorithm leads to a classification accuracy of 86%. Further, we designed a system ViZig that uses these localized anchor points along with a automatically generated list of topics for non-linear video navigation and studied its effectiveness in real-world. Our user studies with 18 participants establish that the proposed video navigation mechanism provides statistically significant time savings as compared to the popularly used time-synched transcript along with youtube-style timeline scrubbing.
Year
Venue
Field
2016
IUI
Automatic summarization,Nonlinear system,Convolutional neural network,Computer science,Timeline,Artificial intelligence,Point of interest,Deep learning,Multimedia,Flowchart,The Internet
DocType
ISBN
Citations 
Conference
978-1-4503-4137-0
3
PageRank 
References 
Authors
0.44
22
6
Name
Order
Citations
PageRank
Kuldeep Yadav1809.95
Ankit Gandhi2162.92
Arijit Biswas374738.43
Kundan Shrivastava441.46
Saurabh Srivastava518419.27
Om Deshmukh65610.55