Paper Info
Title
A new multimedia information data mining method
Abstract
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.
Year
DOI
Venue
2009
10.1145/1543834.1543970
GEC Summit
Keywords
Field
DocType
traditional distance-based agglomerative mining,framework model,mining method,process mixture model,bayesian hierarchical hypothesis testing,bayesian hierarchical framework model,proposed framework model,mining object,model hyperparameters,new multimedia information data,bayesian hierarchical mining part,data mining,virtual reality,predictive distribution,mixture model,bottom up,hypothesis test
Hierarchical clustering,Data mining,Data set,Virtual reality,Hyperparameter,Computer science,Approximate inference,Artificial intelligence,Machine learning,Mixture model,Statistical hypothesis testing,Bayesian probability
Conference
Citations 
PageRank 
References 
1
0.34
12
Authors
5
Name
Order
Citations
PageRank
Jin Longcun131.09
Wanggen Wan212934.04
Cui Bin310.34
Xiaoqing Yu47511.53
Xu Hongwei510.34