An improved scene clustering algorithm based on Bayesian model

In this paper, we propose an improved scene clustering algorithm based on Bayesian model. We present a Bayesian hierarchical framework model for clustering objects in multimedia scene. The Multimedia scene 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 clustering part. This algorithm has several advantages over traditional distance-based agglomerative clustering 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 clustering the model hyperparameters, computing the predictive distribution, and extensions to the framework model. Experimental results on virtual reality multimedia scene data sets demonstrate useful properties of the framework model.