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 clas-sifier, 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 1 and more specifically in a beach/urban scene classification problem.
