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.