A new multimedia information data mining method

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