  Recently spatial pyramid matching (SPM) with scale invariant feature
transform (SIFT) descriptor has been successfully used in image classification.
Unfortunately, the codebook generation and feature quantization procedures
using SIFT feature have the high complexity both in time and space. To address
this problem, in this paper, we propose an approach which combines local binary
patterns (LBP) and three-patch local binary patterns (TPLBP) in spatial pyramid
domain. The proposed method does not need to learn the codebook and feature
quantization processing, hence it becomes very efficient. Experiments on two
popular benchmark datasets demonstrate that the proposed method always
significantly outperforms the very popular SPM based SIFT descriptor method
both in time and classification accuracy.
