Context-dependent tree-structured image classification using the QDA distortion measure and the hidden Markov model

被引:0
|
作者
Ozonat, KM [1 ]
Yoon, SH [1 ]
机构
[1] Stanford Univ, Informat Syst Lab, Dept Elect Engn, Stanford, CA 94305 USA
来源
ICIP: 2004 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1- 5 | 2004年
关键词
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Vector quantization based Oil the Gauss Mixture model (GMM) and the quadratic discriminant analysis (QDA) distortion measure has been shown to perform well in statistical image classification problems. Previous work in this area has concentrated on designing a separate GMM-based vector quantizer using the QDA distortion measure for each Class using full search. We design a single vector quantizer for all classes using a tree-structured algorithm based on the (generalized) BFOS algorithm. This reduces the search complexity, while it increases the correct classification rate. Further. the pruning Stage of Our algorithm takes into account the dependencies between the image blocks assuming a hidden Markov model (HMM). During the test stage, Our algorithm aims to iteratively maximize the joint probability Of Occurrence of. all image blocks based oil the HMM. Our Simulation results indicate that Our algorithm performs better (both in terms of computational complexity and classification rate) when compared to the previously Published algorithms based oil the GMM.
引用
收藏
页码:1887 / 1890
页数:4
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