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 project1 and more specifically in a beach/urban scene classification problem. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Spyrou, E., Borgne, H. L., Mailis, T., Cooke, E., Avrithis, Y., & O’Connor, N. (2005). Fusing MPEG-7 visual descriptors for image classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3697 LNCS, pp. 847–852). https://doi.org/10.1007/11550907_134
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