Abstract
In this paper, a novel algorithm for shape categorization is proposed. This method is based on the detection of perceptual landmarks, which are scale invariant. These landmarks and the parts between them are transformed into a symbolic representation. Shapes are mapped into symbol sequences and a database of shapes is mapped into a set of symbol sequences and therefore it is possible to use support vector machines for categorization. The method here proposed has been evaluated on silhouettes database and achieved the highest recognition result reported with a score of 97.85% for the MPEG-7 shape database. © Springer-Verlag Berlin Heidelberg 2006.
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CITATION STYLE
Daliri, M. R., Delponte, E., Verri, A., & Torre, V. (2006). Shape categorization using string kernels. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4109 LNCS, pp. 297–305). Springer Verlag. https://doi.org/10.1007/11815921_32
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