A Region Thesaurus Approach for High-Level Concept Detection in the Natural Disaster Domain
Lecture notes in computer sciencePublished 30 November 2007Open access
Evaggelos Spyrou, Yannis Avrithis
Citations15
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TL;DR
This paper presents an approach on high-level feature detection using a region thesaurus, using MPEG-7 features locally extracted from segmented regions and for a large set of images to train support vector machine-based feature detectors.
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. © Springer-Verlag Berlin Heidelberg 2007.
Keywords
Computer Science
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