Abstract
Measuring the similarity of two shapes is an important task in human vision systems in order to either recognize or classify the objects. For obtaining reliable results, a high discriminative shape descriptor should be extracted by considering both global and local information of the shape. Taking into account, this work introduces a centroid-based tree-structured (CENTREES) shape descriptor invariant to rotation and scale. Extracting the CENTREES descriptor is started by computing the central of mass of a binary shape, assigned as the root node of tree. The entire shape is then decomposed into b sub-shapes by voting each pixel point according to an angle between point and major principal axis relative to a centroid. In the same way, the central of mass 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, parameters invariant to translation, rotation and scale are extracted. A vector of all parameters is considered as descriptor. A feature-based template matching with X 2 distance function is used to measure shape dissimilarity. The evaluation of our descriptor is conducted using MPEG-7 dataset. The results justify that the CENTREES is one of reliable shape descriptors for shape similarity.
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Wahyono, Kurnianggoro, L., Yang, Y., Jo, KH. (2016). A Similarity-Based Approach for Shape Classification Using Region Decomposition. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_24
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DOI: https://doi.org/10.1007/978-3-319-42294-7_24
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