Joint Key-Frame Extraction and Object Segmentation for Content-Based Video Analysis

Key-frame extraction and object segmentation are usually implemented independently and separately due to the fact that they are on different semantic levels and involve different features. In this work, we propose a joint key-frame extraction and object segmentation method by constructing a unified...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2006-07, Vol.16 (7), p.904-914
Main Authors: Song, X., Fan, G.
Format: Article
Language:English
Subjects:
Applied sciences
Bridges
Circuits
Cluster divergence
Context modeling
Data mining
Density
Divergence
Engineering profession
Exact sciences and technology
Extraction
feature selection
Gaussian
Gaussian mixture model
Humans
Information, signal and communications theory
key-frame extraction
Mathematical models
Military computing
Miscellaneous
Object segmentation
Region 4
Segmentation
Semantics
Shape
Signal processing
Telecommunications and information theory
Time factors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Key-frame extraction and object segmentation are usually implemented independently and separately due to the fact that they are on different semantic levels and involve different features. In this work, we propose a joint key-frame extraction and object segmentation method by constructing a unified feature space for both processes, where key-frame extraction is formulated as a feature selection process for object segmentation in the context of Gaussian mixture model (GMM)-based video modeling. Specifically, two divergence-based criteria are introduced for key-frame extraction. One recommends key-frame extraction that leads to the maximum pairwise interclass divergence between GMM components. The other aims at maximizing the marginal divergence that shows the intra-frame variation of the mean density. The proposed methods can extract representative key-frames for object segmentation, and some interesting characteristics of key-frames are also discussed. This work provides a unique paradigm for content-based video analysis
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2006.877419