A region thesaurus approach for high-level concept detection in the natural disaster domain

Authors:
Evaggelos Spyrou;Yannis Avrithis
Affiliations:
Image, Video and Multimedia Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;Image, Video and Multimedia Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
Venue:
SAMT'07 Proceedings of the semantic and digital media technologies 2nd international conference on Semantic Multimedia
Year:
2007

Citing 4
Cited 2

Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Evaluation campaigns and TRECVid

MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Learned lexicon-driven interactive video retrieval

CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Overview of the MPEG-7 standard

IEEE Transactions on Circuits and Systems for Video Technology

Concept detection and keyframe extraction using a visual thesaurus

Multimedia Tools and Applications
Using visual context and region semantics for high-level concept detection

IEEE Transactions on Multimedia - Special issue on integration of context and content

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents an approach on high-level feature detection using a region thesaurus. MPEG-7 features are locally extracted from segmented regions and for a large set of images. A hierarchical clustering approach is applied and a relatively small number of region types is selected. This set of region types defines the region thesaurus. Using this thesaurus, low-level features are mapped to high-level concepts as model vectors. This representation is then used to train support vector machine-based feature detectors. As a next step, latent semantic analysis is applied on the model vectors, to further improve the analysis performance. High-level concepts detected derive from the natural disaster domain.