计算机科学 ›› 2009, Vol. 36 ›› Issue (7): 273-277.doi: 10.11896/j.issn.1002-137X.2009.07.067

• 图形图像及体系结构 • 上一篇    下一篇

目标语义概率模型在类目标识别和地物场景分析中的算法研究

刘玮,陈新武,田金文   

  1. (华中科技大学图像识别与人工智能研究所多谱信息处理技术国防重点实验室 武汉430074)
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本课题受863项目(2007AA12Z153)资助

Object Semantic Probabilistic Model and its Application in Category Object Recognition and Scene Analysis

LIU Wei,CHEN Xin-wu,TIAN Jin-wen   

  • Online:2018-11-16 Published:2018-11-16

摘要: 基于文本分析统计模型提出了图像类目标的语义概率模型,并且将这种概率模型应用于目标识别和复杂场景下的地物分析。首先将图像表示成多个特征局部区域的集合,然后根据目标语义概率模型得到图像、特征局部和目标语义之间的概率关系,通过计算后验概率可以实现目标语义类别的识别。目标概率模型通过EM算法获得模型估计参数。实验结果显示,在识别复杂背景中的目标达到了很好的效果。场景分析中根据图像中各局部区域与目标语义的概率分布可以实现场景中感兴趣区域的标注,实验结果说明此方法有可行性。

关键词: 类目标识别,场景分析,语义概率模型,图模型

Abstract: The article seek to discover the object categories' semantic probabilistic model based on statistical text analysis and we applied this new model on object recognition and Scene analysis. First, the image was represented by a set composed of local feature regions. Then, found the probability among image, local regions and semantic category based on the new model helps to calculate the posterios and recognizing the object. EM algorithm was used to estimate the parameters of the model. Experiments show the good performance on object recognition in the cluster background and also show the feasibility of the scene analysis.

Key words: Category recognition, Scene analysis, Semantic probabilistic model, Graph model

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