3D Object Classification by Fuzzy KNN and Bayesian Decision

Abstract

This paper presents a fuzzy KNN and Bayesian decision based classification method for determining whether a 3D object belongs to human class. To achieve efficiency and simplicity, the view having maximum area is used to substitute a 3D shape, which is front-side view for human models. View features and structural features are utilized to describe those selected views. For shape feature can not distinguish one class from the others crisply, fuzzy KNN is adopted to estimate the probability that the object belongs to human class using view feature extracted from an unknown object and view features of training objects. Structural features are represented as adjacent graphs after shape decompositions, while characteristic structures are predefined for human class. Next priori probabilities that the characteristic structure exist in human and other classes are gained from training objects. For an unknown object, an adjacent graph is checked whether the characteristic structure exists, and then fuzzy classification results and priori probabilities are used together to classify the unknown object by Bayesian rule. Experimental results show that our approach have good accuracy for classifications.