SPM based on normalized cut for image classification

It is difficult to classify scene images with high accuracy when the dataset is relatively large. Spatial pyramid matching was proposed to deal with this problem, but there are some shortages. As an improvement, the algorithm based on normalized cut was proposed. Normalized cut was utilized instead of K-means for clustering. The size of codebook was regulated referring to quantity and size of the images, by calculating sub-codebook for every category and re-clustering the codes. Distance between categories was enlarged by quantifying unknown features with Gaussian model and rescaling the histogram features. Experiments prove that new approach can get higher precision than the original by 4.6% at most.