Multicore computing for SIFT Algorithm in MATLAB® Parallel Environment

An important processing stage in computer vision such as recognizes an object is feature extraction. Among those feature extraction algorithms, Lowe proposed the scale-invariant feature transform (SIFT) algorithm has been considered as one of the robust approaches. But due to the implementation of the convolution search operation, the computing of SIFT algorithm is highly time-consuming. There are such problems as consuming too much power, sacrifice accuracy and lacking scalability for hardware-based acceleration scheme, it is still necessary to find the software-based acceleration approaches, especially for some applications and researches which need high-precision matched. Matlab is a general algorithm development environment with powerful image processing and other supporting toolboxes. With the rapid development of multicore CPU technology, using multicore computer and Matlab is an intuitive and simple way to speed up the computing for SIFT algorithm. In this paper, we try to have a view for using the Matlab parallel toolbox to accelerate the SIFT algorithm by two schemes of task-parallelism and data-parallelism modal. The results show that the parallel versions of former sequential algorithm with simple modifications achieve the speedup up to 6.6 times