Abstract
This paper proposes three content-based image classification techniques based on fusing various low-level MPEG-7 visual descriptors. Fusion is necessary as descriptors would be otherwise incompatible and inappropriate to directly include e.g. in a Euclidean distance. Three approaches are described: A “merging” fusion combined with an SVM classifier, a back-propagation fusion combined with a KNN classifier and a Fuzzy-ART neurofuzzy network. In the latter case, fuzzy rules can be extracted in an effort to bridge the “semantic gap” between the low-level descriptors and the high-level semantics of an image. All networks were evaluated using content from the repository of the aceMedia project and more specifically in a beach/urban scene classification problem.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Spyrou, E., Le Borgne, H., Mailis, T., Cooke, E., Avrithis, Y., O’Connor, N. (2005). Fusing MPEG-7 Visual Descriptors for Image Classification. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_134
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DOI: https://doi.org/10.1007/11550907_134
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