Issues in Mining Video Datasets
Shawn Newsam*a,
Jelena Tešicb, Lei Wangb, and B. S. Manjunathb
aLawrence Livermore National Laboratory,
7000 East Avenue, Livermore, CA, USA 94551
bDept. of Elec. and Comp. Eng.,
University of California, Santa Barbara, CA, USA 93106-9560
Email: {kenney, manj, zuliani}@ece.ucsb.edu
Abstract
This paper presents an
overview of our recent work
on managing image and video data.
The first half of the paper describes a representation
for the semantic spatial layout of video frames.
In particular, Markov random fields are used to characterize
the spatial arrangement of frame tiles that are labeled
using support vector machine classifiers.
The representation is shown to support similarity retrieval
at the semantic level as demonstrated in a prototype video management
system.
The second half of the paper describes a method for efficiently computing
nearest neighbor queries in high-dimensional feature spaces
in a relevance feedback framework.
