Similarity-Based Classification of 2-D Shape Using Centroid-Based Tree-Structured Descriptor
This paper introduces a novel shape descriptor invariant to rotation and scale, namely centroid-based tree-structured (CbTs), for measuring shape similarity. For obtaining CbTs descriptor, first, the central of mass of a binary shape is computed. It will be regarded as the root node of tree. The shape is divided into b sub-shapes by voting each foreground pixel point based on angle between point and major principal axis. In the same way, the central of masses of the sub-shapes are calculated and these locations are considered as level-1 nodes. These processes are repeated for a predetermined number of levels. For each node corresponding to sub-shapes, five parameters invariant to translation, rotation and scale are extracted. Thus, a vector of all parameters is carried out as descriptor. To measure dissimilarity between shapes, we employ vector-based template matching with X 2 distance measurement. Results are presented for MPEG-7 dataset.
KeywordsShape descriptor shape classification centroid tree-structured
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