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.
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Spyrou, E., Avrithis, Y. (2007). A Region Thesaurus Approach for High-Level Concept Detection in the Natural Disaster Domain. In: Falcidieno, B., Spagnuolo, M., Avrithis, Y., Kompatsiaris, I., Buitelaar, P. (eds) Semantic Multimedia. SAMT 2007. Lecture Notes in Computer Science, vol 4816. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77051-0_7
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DOI: https://doi.org/10.1007/978-3-540-77051-0_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77033-6
Online ISBN: 978-3-540-77051-0
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