1999
@article{ADD1999,
vgclass = {refpap},
vgproject = {cbir},
author = {Yannis S. Avrithis and Anastasios D. Doulamis and Nikolaos
D. Doulamis and Stefanos D. Kollias},
title = {A Stochastic Framework for Optimal Key Frame Extraction
from {MPEG} Video Databases},
journal = {Computer Vision and Image Understanding (special issue on content-based access for image
and video libraries)},
volume = {75},
number = {1/2},
pages = {3--24},
month = {July/August},
year = {1999},
abstract = {A video content representation framework is proposed in
this paper for extracting limited, but meaningful, information of video
data, directly from the MPEG compressed domain. A hierarchical color
and motion segmentation scheme is applied to each video shot,
transforming the frame-based representation to a feature-based one. The
scheme is based on a multiresolution implementation of the recursive
shortest spanning tree (RSST) algorithm. Then, all segment features are
gathered together using a fuzzy multidimensional histogram to reduce
the possibility of classifying similar segments to different classes.
Extraction of several key frames is performed for each shot in a
content-based rate-sampling framework. Two approaches are examined for
key frame extraction. The first is based on examination of the temporal
variation of the feature vector trajectory; the second is based on
minimization of a cross-correlation criterion of the video frames. For
efficient implementation of the latter approach, a logarithmic search
(along with a stochastic version) and a genetic algorithm are proposed.
Experimental results are presented which illustrate the performance of
the proposed techniques, using synthetic and real life MPEG video
sequences.},
}