Multi-pyramid image spatial structure based on coarse-to-fine pyramid and scale space

Abstract

Coarse-to-fine pyramid and scale space are two important image structures in the realm of image matching. However, the advantage of coarse-to-fine pyramid is neglected as the pyramid structure is usually constructed with the down sampling method in scale space. In addition, the importance of each lattice is different for one single image. Based on the analyses above, the new multi-pyramid (M-P) image spatial structure is constructed. First, coarse-to-fine pyramid is constructed by partitioning the original image into increasingly finer lattices, and the number of interest points is also adopted to be each lattice's non-normalized weight on each pyramid level. Second, the scale space of each lattice on each pyramid level is generated with the classic Gaussian kernel. Third, the descriptors of each lattice are generated by regarding the stability of scale space as the description of image. Moreover, the parallel version of M-P algorithm is also presented to accelerate the speed of computation. Finally, the comprehensive experimental results reveal that our multi-pyramid structure which is constructed by the combination of coarse-to-fine spatial pyramid and scale space can generate more effective features, compared with the other related methods.

Keywords

• Gaussian processes
• feature extraction
• image matching
• pyramid structure
• scale space
• pyramid level
• coarse-to-fine spatial pyramid
• multipyramid image spatial structure
• image matching