Novel Spatial Pyramid Matching for scene and object classification

It is difficult to classify object or scene images with high accuracy when the dataset is relatively large. Spatial Pyramid Matching (SPM) was proposed to deal with this problem, but there are some shortages. As an improvement for SPM, we proposed three pieces of meliorations: first, use approximate nearest neighbor method instead of k-means for clustering; second, regulate the size of codebook referring to quantity and pixels of the images, by calculating sub-codebook for every category and eliminating the codes which are nearer to the registered ones than the threshold; third, rescale the histogram features, and classify the scene with hierarchical strategy. Experiments prove that our approach make better performance than other state-of-the-art classification methods using just one matching kernel.