中文摘要：

针对真实场景图像的目标分类问题,提出一种基于多尺度上下文信息的分类算法.首先运用一种软判决采样机制对图像进行局部信息采样,使场景内混合的各类信息以一种鲁棒的方式得到有效分离;然后,进一步基于软判决采样和统计特征表达机制,计算各空间尺度下的目标上下文统计特征;最后,通过逻辑回归分类算法有效地融合多尺度的上下文信息,并作出分类决策.实验表明,所提出的算法能更好地刻画真实场景下目标的特性,明显提高图像目标分类性能.

英文摘要：

To categorize objects in the real-world scene images,a method is proposed by exploiting multi-spatial extent context.Firstly,a soft decision-based sampling mechanism is utilized in the local image patch sampling process,by which,mixed information in the scene can be separated in an effective and robust way.Then,by using the soft decision-based sampling mechanism and the statistical representation methods,the statistical feature for each spatial extent context can be computed.Finally,a logistic regression classification method is adopted to integrate multiple spatial extent context information and make the final decisions.The experiments show that,the proposed method can better model the objects in the real world scenes,and thus apparently improves the object categorization performance.