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Using Shape Descriptors in Shape Data Classification
Meisam Moghimbeygi *
Abstract:   (57 Views)

The classification of shape data is a significant challenge in the statistical analysis of shapes and machine learning. In this paper, we introduce a multinomial logistic regression model based on shape descriptors for classifying labeled configurations. In this model, the explanatory variables include a set of geometric descriptors such as area, elongation, convexity, and circularity, while the response variable represents the category of each configuration. The inclusion of these descriptors preserves essential geometric information and enhances classification accuracy. We evaluate the proposed model using both simulated data and real datasets, and the results demonstrate its effective performance. Additionally, the proposed method was compared with one of the existing methods in the literature, and the results indicated its superiority in terms of both classification accuracy and computational simplicity.

Keywords: Logistic regression, shape descriptor, shape data, classification.
Full-Text [PDF 3132 kb]   (27 Downloads)    
Type of Study: Applied | Subject: Applied Statistics
Received: 2024/07/13 | Accepted: 2025/04/30
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مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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