Classification of images based on Hidden Markov Models

被引：8
|
作者：
Mouret, Marc [1 ]
Solnon, Christine [1 ]
Wolf, Christian [1 ]
机构：
[1] Univ Lyon, CNRS, Lyon, France
来源：
CBMI: 2009 INTERNATIONAL WORKSHOP ON CONTENT-BASED MULTIMEDIA INDEXING | 2009年
关键词：
DISTANCES;
D O I：
10.1109/CBMI.2009.22
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We propose to use hidden Markov models (HMMs) to classify images. Images are modeled by extracting symbols corresponding to 3 x 3 binary neighborhoods of interest points, and by ordering these symbols by decreasing saliency order, thus obtaining strings of symbols. HMMs are learned from sets of strings modeling classes of images. The method has been tested on the SIMPLIcity database and shows an improvement over competing approaches based on interest points. We also evaluate these approaches for classifying thumbnail images, i.e., low resolution images.
引用
收藏
页码：169 / 174
页数：6
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