Classification of silhouettes using contour fragments

In this paper, we propose a fragment-based approach for classification and recognition of shape contours. According to this method, first the perceptual landmarks along the contours are localized in a scale invariant manner, which makes it possible to extracts the contour fragments. Using a predefined dictionary for the fragments, these landmarks and the parts between them are transformed into a symbolic representation that is a compact representation. Using a string kernel-like approach, an invariant high-dimensional feature space is created from the symbolic representation and later the most relevant lower dimensions are extracted by principal component analysis. Finally, support vector machine is used for classification of the feature space. The experimental results show that the proposed method has similar performance to the best approaches for shape recognitions while it has lower complexity.