基于高斯统计模型的场景分类及 约束机制新方法

 针对场景分类问题,提出了一种基于高斯统计模型的方法,无需手动标记特征表示的场景类别信息,通过无监督聚类和高斯模型的参数学习建立场景语义和"基元"信息的概率对应关系,实验结果说明,提出的方法较其他有监督的场景分类方法高效,与复杂层状模型下的分类结果非常近似.同时,初步研究了场景分类对目标分析的约束机制,通过实验证明场景信息能为目标分析提供有效的先验知识,限定不同场景下目标出现的可能性,提高了目标分类的准确性.

A Novel Method for Scene Categorization with Constraint Mechanism Based on Gaussian Statistical Model

Focusing on scene categorization,we presented a method based on Gaussian statistic model.Without feature hand-annotation for scene category,we can use unsupervised clustering and parameter estimation in Gaussian model to build corresponding probabilistic relations between scene labelling semantics and "textons" information.The experiments reveal that the proposed method performs more efficiently than other supervised ones and has the quite approximate results with complex hierarchical models.At the same tim...