| E. Spyrou, H. Le Borgne, T. Mailis, E. Cooke, Y. Avrithis, and N. O'Connor |
| Fusing MPEG-7 visual descriptors for image classiffication |
| Proc. of International Conference on Artificial Neural Networks (ICANN ’05), Warsaw, Poland, September 11-15, 2005 |
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ABSTRACT
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| This paper proposes a number of content-based image classification techniques based on fusing various low-level MPEG-7 visual descriptors. The goal is to fuse several descriptors in order to improve the performance of several machine-learning classifiers. 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 K-Nearest Neighbor 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 aceMedia Repositoryand more specifically in a beach/urban scenes classification problem.
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| 11 September, 2005 |
| E. Spyrou, H. Le Borgne, T. Mailis, E. Cooke, Y. Avrithis, and N. O'Connor, "Fusing MPEG-7 visual descriptors for image classiffication", Proc. of International Conference on Artificial Neural Networks (ICANN ’05), Warsaw, Poland, September 11-15, 2005 |
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