基于多尺度上下文信息的图像目标分类算法

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.

Object categorization using context from multi-spatial levels

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 cl...