Techniques for designing a classifier for multimedia indexing
A. Mittal, L. Cheong
2001
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Key Takeaway Key Takeaway: The Modified Bayesian Network (MBN) offers significant advantages over other approaches in multimedia indexing due to its ability to represent non-Gaussian multimodal continuous distributions and intelligent partitioning.
Abstract
This paper addresses the issues involved in designing a classifier for multimedia indexing, a representative of domain of tasks involving high dimensionality of feature space and large dissimilarity between features in range and variation, and requiring a strong inference mechanisms. We consider decision trees, Bayesian network, neural network and support vector approaches. The Modified Bayesian Network (MBN), as designed by us offers significant advantages over other approaches. The application of Bayesian network has generally been restricted to domains having discrete variable values, or to the domain with continuos variable values which approximate to Gaussian distribution. However, MBN can form sound representation of non-Gaussian Multimodal continuous distribution, as is the case with feature space in multimedia indexing. This can be accomplished by intelligent partitioning and data clique association. The structure of MBN and its functionality on real video is also presented in the paper.