Cloud Model-Based Feature Extraction Algorithm for Flower Petals

This article introduces a cloud model-based feature extraction algorithm for flower petals implemented through a reverse normal cloud generator. The algorithm collects sample point data and calculates three key digital characteristics - expected value (Ex), entropy (En), and hyper-entropy (He) - for each of the four feature parameters across three flower categories. The implementation typically involves statistical processing of sample distributions followed by cloud transformation calculations to handle uncertainty in feature representation. This algorithm's application enhances our understanding of floral characteristics and properties, providing more accurate data for flower research and classification. Additionally, the methodology can be extended to other domains such as image recognition and pattern recognition, where uncertainty handling and feature quantification are essential. Consequently, this cloud model-based petal feature extraction algorithm demonstrates broad application prospects and significant research value.