Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Shape skeletons are commonly used in generic shape recognition as they capture part hierarchy, providing a structural representation of shapes. Hover, their potential for shape classification has not been investigated much. In this study,  present a similarity-based approach for classifying 2D shapes based on their Aslan skeletons (Aslan and Tari, 2005; Aslan et al., 2008). The coarse structure of this skeleton representation allows us to represent each shape category in the form of a reduced set of prototypical trees, offering an alternative solution to the problem of selecting the best representative examples. The ensemble of these category prototypes is then used to form a similarity-based representation space in which the similarities beten a given shape and the prototypes are computed using a tree edit distance algorithm, and support vector machine (SVM) classifiers are used to predict the category membership of the shape based on computed similarities.
['Aykut Erdem', 'Sibel Tari']
I am writing a research paper on a idea A similarity-based approach for shape classification using Aslan skeletons
[{"from": "human", "value": "I am writing a research paper on a idea A similarity-based approach for shape classification using Aslan skeletons"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Shape skeletons are commonly used in generic shape recognition as they capture part hierarchy, providing a structural representation of shapes. Hover, their potential for shape classification has not been investigated much. In this study,  present a similarity-based approach for classifying 2D shapes based on their Aslan skeletons (Aslan and Tari, 2005; Aslan et al., 2008). The coarse structure of this skeleton representation allows us to represent each shape category in the form of a reduced set of prototypical trees, offering an alternative solution to the problem of selecting the best representative examples. The ensemble of these category prototypes is then used to form a similarity-based representation space in which the similarities beten a given shape and the prototypes are computed using a tree edit distance algorithm, and support vector machine (SVM) classifiers are used to predict the category membership of the shape based on computed similarities."}]