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Abstract
In this paper, we propose a Bayesian of inductive cognition algorithm based on Dirichlet process used in virtual reality multimedia information data classification. We present a Bayesian of inductive cognition algorithm framework model for classifying scenes in virtual reality multimedia data. The multimedia can switch between different shots, the unknown objects can leave or enter the scene at multiple times, and the scenes can be classified. The proposed algorithm consists of Bayesian inductive cognition part and Dirichlet process part. This algorithm has several advantages over traditional distance-based agglomerative classifying algorithms. Bayesian of inductive cognition algorithm based on Dirichlet process hypothesis testing is used to decide which merges are advantageous and to output the recommended depth of the scenes. The algorithm can be interpreted as a novel fast bottom-up approximate inference method for a Dirichlet process mixture model. We describe procedures for learning the model hyperparameters, computing the predictive distribution and extensions to the Bayesian of inductive cognition algorithm. Experimental results on virtual reality multimedia datasets demonstrate useful properties of the Bayesian of inductive cognition algorithm.